WorldWideScience

Sample records for bayesian changepoint analysis

  1. Speech Segmentation Using Bayesian Autoregressive Changepoint Detector

    Directory of Open Access Journals (Sweden)

    P. Sovka

    1998-12-01

    Full Text Available This submission is devoted to the study of the Bayesian autoregressive changepoint detector (BCD and its use for speech segmentation. Results of the detector application to autoregressive signals as well as to real speech are given. BCD basic properties are described and discussed. The novel two-step algorithm consisting of cepstral analysis and BCD for automatic speech segmentation is suggested.

  2. Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models.

    Science.gov (United States)

    Lock, Eric F; Kohli, Nidhi; Bose, Maitreyee

    2017-11-17

    Piecewise growth mixture models are a flexible and useful class of methods for analyzing segmented trends in individual growth trajectory over time, where the individuals come from a mixture of two or more latent classes. These models allow each segment of the overall developmental process within each class to have a different functional form; examples include two linear phases of growth, or a quadratic phase followed by a linear phase. The changepoint (knot) is the time of transition from one developmental phase (segment) to another. Inferring the location of the changepoint(s) is often of practical interest, along with inference for other model parameters. A random changepoint allows for individual differences in the transition time within each class. The primary objectives of our study are as follows: (1) to develop a PGMM using a Bayesian inference approach that allows the estimation of multiple random changepoints within each class; (2) to develop a procedure to empirically detect the number of random changepoints within each class; and (3) to empirically investigate the bias and precision of the estimation of the model parameters, including the random changepoints, via a simulation study. We have developed the user-friendly package BayesianPGMM for R to facilitate the adoption of this methodology in practice, which is available at https://github.com/lockEF/BayesianPGMM . We describe an application to mouse-tracking data for a visual recognition task.

  3. Bayesian change-point analysis reveals developmental change in a classic theory of mind task.

    Science.gov (United States)

    Baker, Sara T; Leslie, Alan M; Gallistel, C R; Hood, Bruce M

    2016-12-01

    Although learning and development reflect changes situated in an individual brain, most discussions of behavioral change are based on the evidence of group averages. Our reliance on group-averaged data creates a dilemma. On the one hand, we need to use traditional inferential statistics. On the other hand, group averages are highly ambiguous when we need to understand change in the individual; the average pattern of change may characterize all, some, or none of the individuals in the group. Here we present a new method for statistically characterizing developmental change in each individual child we study. Using false-belief tasks, fifty-two children in two cohorts were repeatedly tested for varying lengths of time between 3 and 5 years of age. Using a novel Bayesian change point analysis, we determined both the presence and-just as importantly-the absence of change in individual longitudinal cumulative records. Whenever the analysis supports a change conclusion, it identifies in that child's record the most likely point at which change occurred. Results show striking variability in patterns of change and stability across individual children. We then group the individuals by their various patterns of change or no change. The resulting patterns provide scarce support for sudden changes in competence and shed new light on the concepts of "passing" and "failing" in developmental studies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  4. Bayesian unknown change-point models to investigate immediacy in single case designs.

    Science.gov (United States)

    Natesan, Prathiba; Hedges, Larry V

    2017-12-01

    Although immediacy is one of the necessary criteria to show strong evidence of a causal relation in single case designs (SCDs), no inferential statistical tool is currently used to demonstrate it. We propose a Bayesian unknown change-point model to investigate and quantify immediacy in SCD analysis. Unlike visual analysis that considers only 3-5 observations in consecutive phases to investigate immediacy, this model considers all data points. Immediacy is indicated when the posterior distribution of the unknown change-point is narrow around the true value of the change-point. This model can accommodate delayed effects. Monte Carlo simulation for a 2-phase design shows that the posterior standard deviations of the change-points decrease with increase in standardized mean difference between phases and decrease in test length. This method is illustrated with real data. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  5. A novel Bayesian change-point algorithm for genome-wide analysis of diverse ChIPseq data types.

    Science.gov (United States)

    Xing, Haipeng; Liao, Willey; Mo, Yifan; Zhang, Michael Q

    2012-12-10

    ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein(1). For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment(2). Reliably identifying these regions was the focus of our work. Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics(3-5) to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)(6-8). We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized. Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types. To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs(9), which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how

  6. Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of haemolytic uraemic syndrome.

    Science.gov (United States)

    Tian, Guo-Liang; Ng, Kai Wang; Li, Kai-Can; Tan, Ming

    2009-07-01

    Diarrhoea-associated Haemolytic uraemic syndrome (HUS) is a disease that affects the kidneys and other organs. Motivated by the annual number of cases of HUS collected in Birmingham and Newcastle of England, respectively, from 1970 to 1989, we consider Bayesian changepoint analysis with specific attention to Poisson changepoint models. For changepoint models with unknown number of changepoints, we propose a new non-iterative Bayesian sampling approach (called exact IBF sampling), which completely avoids the problem of convergence and slow convergence associated with iterative Markov chain Monte Carlo (MCMC) methods. The idea is to first utilize the sampling inverse Bayes formula (IBF) to derive the conditional distribution of the latent data given the observed data, and then to draw iid samples from the complete-data posterior distribution. For the purpose of selecting the appropriate model (or determining the number of changepoints), we develop two alternative formulae to exactly calculate marginal likelihood (or Bayes factor) by using the exact IBF output and the point-wise IBF, respectively. The HUS data are re-analyzed using the proposed methods. Simulations are implemented to validate the performance of the proposed methods.

  7. Sample size and classification error for Bayesian change-point models with unlabelled sub-groups and incomplete follow-up.

    Science.gov (United States)

    White, Simon R; Muniz-Terrera, Graciela; Matthews, Fiona E

    2018-05-01

    Many medical (and ecological) processes involve the change of shape, whereby one trajectory changes into another trajectory at a specific time point. There has been little investigation into the study design needed to investigate these models. We consider the class of fixed effect change-point models with an underlying shape comprised two joined linear segments, also known as broken-stick models. We extend this model to include two sub-groups with different trajectories at the change-point, a change and no change class, and also include a missingness model to account for individuals with incomplete follow-up. Through a simulation study, we consider the relationship of sample size to the estimates of the underlying shape, the existence of a change-point, and the classification-error of sub-group labels. We use a Bayesian framework to account for the missing labels, and the analysis of each simulation is performed using standard Markov chain Monte Carlo techniques. Our simulation study is inspired by cognitive decline as measured by the Mini-Mental State Examination, where our extended model is appropriate due to the commonly observed mixture of individuals within studies who do or do not exhibit accelerated decline. We find that even for studies of modest size ( n = 500, with 50 individuals observed past the change-point) in the fixed effect setting, a change-point can be detected and reliably estimated across a range of observation-errors.

  8. Sequential Analysis: Hypothesis Testing and Changepoint Detection

    Science.gov (United States)

    2014-07-11

    ones are the P-wave and the S-wave. The P-wave is polarized in the source-to-receiver direction, namely from the epicenter of the earth - quake to the...a bound on the average frequency of false alarms. The theoretical study of quickest changepoint detection has been initiated in two different direc...detection techniques to run at high speeds and with low delay, combined with the generally low frequency of intrusion attempts, presents an interesting

  9. Growth Curve Analysis and Change-Points Detection in Extremes

    KAUST Repository

    Meng, Rui

    2016-05-15

    The thesis consists of two coherent projects. The first project presents the results of evaluating salinity tolerance in barley using growth curve analysis where different growth trajectories are observed within barley families. The study of salinity tolerance in plants is crucial to understanding plant growth and productivity. Because fully-automated smarthouses with conveyor systems allow non-destructive and high-throughput phenotyping of large number of plants, it is now possible to apply advanced statistical tools to analyze daily measurements and to study salinity tolerance. To compare different growth patterns of barley variates, we use functional data analysis techniques to analyze the daily projected shoot areas. In particular, we apply the curve registration method to align all the curves from the same barley family in order to summarize the family-wise features. We also illustrate how to use statistical modeling to account for spatial variation in microclimate in smarthouses and for temporal variation across runs, which is crucial for identifying traits of the barley variates. In our analysis, we show that the concentrations of sodium and potassium in leaves are negatively correlated, and their interactions are associated with the degree of salinity tolerance. The second project studies change-points detection methods in extremes when multiple time series data are available. Motived by the scientific question of whether the chances to experience extreme weather are different in different seasons of a year, we develop a change-points detection model to study changes in extremes or in the tail of a distribution. Most of existing models identify seasons from multiple yearly time series assuming a season or a change-point location remains exactly the same across years. In this work, we propose a random effect model that allows the change-point to vary from year to year, following a given distribution. Both parametric and nonparametric methods are developed

  10. Journal Afrika Statistika ISSN 0852-0305 Bayesian change-point ...

    African Journals Online (AJOL)

    , 30,. 141-146. Page, E.S., 1954. Continuous inspection shemes, Biometrika, 41, 100-115. Rigaill, G., Lebarbier, E. and Robin, S., 2012. Exact posterior distributions and model selection criteria for multiple change-point detection problems.

  11. Confidence Sets for a Change-Point.

    Science.gov (United States)

    1986-10-01

    by the two coonfidence sets are slightly different. Raferty and Akman (1986) give a flat prior Bayesian analysis of these data. It appears% from...simple cumulative sum type statistic for the change-point problem -’-"C with zero-one observations, Biometrika 67, 79-84. Raferty, A. E. and Akman , V

  12. A mixture of delta-rules approximation to bayesian inference in change-point problems.

    Directory of Open Access Journals (Sweden)

    Robert C Wilson

    Full Text Available Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven 'Delta' rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.

  13. Demographic transition in India: an evolutionary interpretation of population and health trends using 'change-point analysis'.

    Science.gov (United States)

    Goli, Srinivas; Arokiasamy, Perianayagam

    2013-01-01

    Lack of a robust analytical tool for trend analysis of population and health indicators is the basic rationale of this study. In an effort to fill this gap, this study advances 'Change-Point analyzer' as a new analytical tool for assessment of the progress and its pattern in population and health indicators. The defining feature of 'change-point analyzer' is that, it detects subtle changes that are often missed in simple trend line plots and also quantified the volume of change that is not possible in simple trend line plots. A long-term assessment of 'change-point analyses' of trends in population and health indicators such as IMR, Population size, TFR, and LEB in India show multiple points of critical changes. Measured change points of demographic and health trends helps in understanding the demographic transitional shifts connecting it to contextual policy shifts. Critical change-points in population and health indicators in India are associated with the evolution of structural changes in population and health policy framework. This study, therefore, adds significantly to the evolutionary interpretation of critical change-points in long-term trajectories of population and health indicators vis-a-vis population and health policy shifts in India. The results have not only helped in reassessing the historical past and the current demographic transition trajectory but also advanced a new method of assessing the population and health trends which are necessary for robust monitoring of the progress in population and health policies.

  14. Change-point analysis data of neonatal diffusion tensor MRI in preterm and term-born infants

    Directory of Open Access Journals (Sweden)

    Dan Wu

    2017-06-01

    Full Text Available The data presented in this article are related to the research article entitled “Mapping the Critical Gestational Age at Birth that Alters Brain Development in Preterm-born Infants using Multi-Modal MRI” (Wu et al., 2017 [1]. Brain immaturity at birth poses critical neurological risks in the preterm-born infants. We used a novel change-point model to analyze the critical gestational age at birth (GAB that could affect postnatal development, based on diffusion tensor MRI (DTI acquired from 43 preterm and 43 term-born infants in 126 brain regions. In the corresponding research article, we presented change-point analysis of fractional anisotropy (FA and mean diffusivities (MD measurements in these infants. In this article, we offered the relative changes of axonal and radial diffusivities (AD and RD in relation to the change of FA and FA-based change-points, and we also provided the AD- and RD-based change-point results.

  15. Near Real Time Change-Point detection in Optical and Thermal Infrared Time Series Using Bayesian Inference over the Dry Chaco Forest

    Science.gov (United States)

    Barraza Bernadas, V.; Grings, F.; Roitberg, E.; Perna, P.; Karszenbaum, H.

    2017-12-01

    The Dry Chaco region (DCF) has the highest absolute deforestation rates of all Argentinian forests. The most recent report indicates a current deforestation rate of 200,000 Ha year-1. In order to better monitor this process, DCF was chosen to implement an early warning program for illegal deforestation. Although the area is intensively studied using medium resolution imagery (Landsat), the products obtained have a yearly pace and therefore unsuited for an early warning program. In this paper, we evaluated the performance of an online Bayesian change-point detection algorithm for MODIS Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) datasets. The goal was to to monitor the abrupt changes in vegetation dynamics associated with deforestation events. We tested this model by simulating 16-day EVI and 8-day LST time series with varying amounts of seasonality, noise, length of the time series and by adding abrupt changes with different magnitudes. This model was then tested on real satellite time series available through the Google Earth Engine, over a pilot area in DCF, where deforestation was common in the 2004-2016 period. A comparison with yearly benchmark products based on Landsat images is also presented (REDAF dataset). The results shows the advantages of using an automatic model to detect a changepoint in the time series than using only visual inspection techniques. Simulating time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes, revealed that this model is robust against noise, and is not influenced by changes in amplitude of the seasonal component. Furthermore, the results compared favorably with REDAF dataset (near 65% of agreement). These results show the potential to combine LST and EVI to identify deforestation events. This work is being developed within the frame of the national Forest Law for the protection and sustainable development of Native Forest in Argentina in

  16. Bayesian Mediation Analysis

    Science.gov (United States)

    Yuan, Ying; MacKinnon, David P.

    2009-01-01

    In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…

  17. Identifying drought response of semi-arid aeolian systems using near-surface luminescence profiles and changepoint analysis, Nebraska Sandhills.

    Science.gov (United States)

    Buckland, Catherine; Bailey, Richard; Thomas, David

    2017-04-01

    Two billion people living in drylands are affected by land degradation. Sediment erosion by wind and water removes fertile soil and destabilises landscapes. Vegetation disturbance is a key driver of dryland erosion caused by both natural and human forcings: drought, fire, land use, grazing pressure. A quantified understanding of vegetation cover sensitivities and resultant surface change to forcing factors is needed if the vegetation and landscape response to future climate change and human pressure are to be better predicted. Using quartz luminescence dating and statistical changepoint analysis (Killick & Eckley, 2014) this study demonstrates the ability to identify step-changes in depositional age of near-surface sediments. Lx/Tx luminescence profiles coupled with statistical analysis show the use of near-surface sediments in providing a high-resolution record of recent system response and aeolian system thresholds. This research determines how the environment has recorded and retained sedimentary evidence of drought response and land use disturbances over the last two hundred years across both individual landforms and the wider Nebraska Sandhills. Identifying surface deposition and comparing with records of climate, fire and land use changes allows us to assess the sensitivity and stability of the surface sediment to a range of forcing factors. Killick, R and Eckley, IA. (2014) "changepoint: An R Package for Changepoint Analysis." Journal of Statistical Software, (58) 1-19.

  18. Bayesian data analysis for newcomers.

    Science.gov (United States)

    Kruschke, John K; Liddell, Torrin M

    2018-02-01

    This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

  19. Paint it Black : Using Change-Point Analysis to Investigate Increasing Vulnerability to Depression towards the End of Vincent van Gogh's Life

    NARCIS (Netherlands)

    van Emmerik, Arnold A P; Hamaker, Ellen

    2017-01-01

    This study investigated whether Vincent van Gogh became increasingly self-focused-and thus vulnerable to depression-towards the end of his life, through a quantitative analysis of his written pronoun use over time. A change-point analysis was conducted on the time series formed by the pronoun use in

  20. Bayesian Exploratory Factor Analysis

    DEFF Research Database (Denmark)

    Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.

    2014-01-01

    This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corr......This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor......, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates...

  1. Bayesian Exploratory Factor Analysis

    Science.gov (United States)

    Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi

    2014-01-01

    This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. PMID:25431517

  2. Bayesian logistic regression analysis

    NARCIS (Netherlands)

    Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.

    2012-01-01

    In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an

  3. Bayesian Independent Component Analysis

    DEFF Research Database (Denmark)

    Winther, Ole; Petersen, Kaare Brandt

    2007-01-01

    In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...... in a Matlab toolbox, is demonstrated for non-negative decompositions and compared with non-negative matrix factorization.......In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...

  4. Bayesian nonparametric data analysis

    CERN Document Server

    Müller, Peter; Jara, Alejandro; Hanson, Tim

    2015-01-01

    This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.

  5. Change-Point and Trend Analysis on Annual Maximum Discharge in Continental United States

    Science.gov (United States)

    Serinaldi, F.; Villarini, G.; Smith, J. A.; Krajewski, W. F.

    2008-12-01

    Annual maximum discharge records from 36 stations representing different hydro-climatic regimes in the continental United States with at least 100 years of records are used to investigate the presence of temporal trends and abrupt changes in mean and variance. Change point analysis is performed by means of two non- parametric (Pettitt and CUSUM), one semi-parametric (Guan), and two parametric (Rodionov and Bayesian Change Point) tests. Two non-parametric (Mann-Kendall and Spearman) and one parametric (Pearson) tests are applied to detect the presence of temporal trends. Generalized Additive Model for Location Scale and Shape (GAMLSS) models are also used to parametrically model the streamflow data exploiting their flexibility to account for changes and temporal trends in the parameters of distribution functions. Additionally, serial correlation is assessed in advance by computing the autocorrelation function (ACF), and the Hurst parameter is estimated using two estimators (aggregated variance and differenced variance methods) to investigate the presence of long range dependence. The results of this study indicate lack of long range dependence in the maximum streamflow series. At some stations the authors found a statistically significant change point in the mean and/or variance, while in general they detected no statistically significant temporal trends.

  6. Bayesian Data Analysis (lecture 1)

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.

  7. Bayesian Data Analysis (lecture 2)

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.

  8. Change-point analysis of geophysical time series: application to landslide displacement rate (Séchilienne rock avalanche, France)

    Science.gov (United States)

    Amorese, D.; Grasso, J.-R.; Garambois, S.; Font, M.

    2018-02-01

    The rank sum multiple change-point method is a robust statistical procedure designed to search for the optimal number and the location of change-points in an arbitrary continue or discrete sequence of values. As such, this procedure can be used to analyze time series data. Twelve years of robust data sets for the Séchilienne (French Alps) rockslide show a continuous increase in average displacement rate from 50 to 280 mm per month, in the 2004 - 2014 period, followed by a strong decrease back to 50 mm per month in the 2014 - 2015 period. When possible kinematic phases are tentatively suggested in previous studies, its solely rely on the basis of empirical threshold values. In this paper, we analyse how the use of a statistical algorithm for change point detection helps to better understand time phases in landslide kinematics. First we test the efficiency of the statistical algorithm on geophysical benchmark data, these data sets (stream flows, northern hemisphere temperatures) being already analysed by independent statistical tools. Second, we apply the method to 12-year daily time series of the Séchilienne landslide, for rainfall and displacement data, from December 2003 to December 2015, in order to quantitatively extract changes in landslide kinematics.We find two strong significant discontinuities in the weekly cumulated rainfall values: an average rainfall rate increase is resolved in April 2012 and a decrease in August 2014. Four robust changes are highlighted in the displacement time series (May 2008, November-December 2009-January 2010, September 2012 and March 2014), the 2010 one being preceded by a significant but weak rainfall rate increase (in November 2009). Accordingly, we are able to quantitatively define 5 kinematic stages for the Séchilienne rock avalanche during this period. The synchronisation between the rainfall and displacement rate, only resolved at the end of 2009 and beginning of 2010, corresponds to a remarkable change (fourfold

  9. Sexually selected signaling in birds: A case for bayesian change-point analysis of behavioral routines

    NARCIS (Netherlands)

    Roth, T.; Sprau, P.; Naguib, M.; Amrhein, V.

    2012-01-01

    Responses of organisms to environments or to conspecifics may abruptly change once the organism has changed its state. For example, the expression of sexually selected signals often depends on the pairing status of the sender. A likely change in signaling routines at the point of pair formation

  10. Sexually selected signaling in birds: a case for Bayesian change-point analysis of behavioral routinges

    NARCIS (Netherlands)

    Roth, T.; Sprau, P.; Naguib, M.; Amrhein, V.

    2012-01-01

    Responses of organisms to environments or to conspecifics may abruptly change once the organism has changed its state. For example, the expression of sexually selected signals often depends on the pairing status of the sender. A likely change in signaling routines at the point of pair formation

  11. Bayesian analysis of CCDM models

    Energy Technology Data Exchange (ETDEWEB)

    Jesus, J.F. [Universidade Estadual Paulista (Unesp), Câmpus Experimental de Itapeva, Rua Geraldo Alckmin 519, Vila N. Sra. de Fátima, Itapeva, SP, 18409-010 Brazil (Brazil); Valentim, R. [Departamento de Física, Instituto de Ciências Ambientais, Químicas e Farmacêuticas—ICAQF, Universidade Federal de São Paulo (UNIFESP), Unidade José Alencar, Rua São Nicolau No. 210, Diadema, SP, 09913-030 Brazil (Brazil); Andrade-Oliveira, F., E-mail: jfjesus@itapeva.unesp.br, E-mail: valentim.rodolfo@unifesp.br, E-mail: felipe.oliveira@port.ac.uk [Institute of Cosmology and Gravitation—University of Portsmouth, Burnaby Road, Portsmouth, PO1 3FX United Kingdom (United Kingdom)

    2017-09-01

    Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3α H {sub 0} model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

  12. Multiview Bayesian Correlated Component Analysis

    DEFF Research Database (Denmark)

    Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai

    2015-01-01

    are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....

  13. Bayesian analysis of rare events

    Science.gov (United States)

    Straub, Daniel; Papaioannou, Iason; Betz, Wolfgang

    2016-06-01

    In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

  14. Assessment of heterogeneity of residual variances using changepoint techniques

    Directory of Open Access Journals (Sweden)

    Toro Miguel A

    2000-07-01

    Full Text Available Abstract Several studies using test-day models show clear heterogeneity of residual variance along lactation. A changepoint technique to account for this heterogeneity is proposed. The data set included 100 744 test-day records of 10 869 Holstein-Friesian cows from northern Spain. A three-stage hierarchical model using the Wood lactation function was employed. Two unknown changepoints at times T1 and T2, (0 T1 T2 tmax, with continuity of residual variance at these points, were assumed. Also, a nonlinear relationship between residual variance and the number of days of milking t was postulated. The residual variance at a time t( in the lactation phase i was modeled as: for (i = 1, 2, 3, where λι is a phase-specific parameter. A Bayesian analysis using Gibbs sampling and the Metropolis-Hastings algorithm for marginalization was implemented. After a burn-in of 20 000 iterations, 40 000 samples were drawn to estimate posterior features. The posterior modes of T1, T2, λ1, λ2, λ3, , , were 53.2 and 248.2 days; 0.575, -0.406, 0.797 and 0.702, 34.63 and 0.0455 kg2, respectively. The residual variance predicted using these point estimates were 2.64, 6.88, 3.59 and 4.35 kg2 at days of milking 10, 53, 248 and 305, respectively. This technique requires less restrictive assumptions and the model has fewer parameters than other methods proposed to account for the heterogeneity of residual variance during lactation.

  15. Confidence Sets in Change-Point Problems. Revision.

    Science.gov (United States)

    1987-05-01

    Bayesian credible sets for the change-point have been considered by Smith (1975) and Raferty and Akman (1986). Although some numerical computation is...essentially the same as the confidence set computed here. Actually Raferty and Akman consider a continuous time Poisson process model and hence allow...cumulative sum type statisti( for t t’ie iig point prodli-ii with zero-one observations. lhumetrka 87. 79-S4 RAferty. A. E and Akman . V. E. (1986

  16. Paint it Black: Using Change-Point Analysis to Investigate Increasing Vulnerability to Depression towards the End of Vincent van Gogh’s Life

    Directory of Open Access Journals (Sweden)

    Arnold A. P. van Emmerik

    2017-09-01

    Full Text Available This study investigated whether Vincent van Gogh became increasingly self-focused—and thus vulnerable to depression—towards the end of his life, through a quantitative analysis of his written pronoun use over time. A change-point analysis was conducted on the time series formed by the pronoun use in Van Gogh’s letters. We used time as a predictor to see whether there was evidence for increased self-focus towards the end of Van Gogh’s life, and we compared this to the pattern in the letters written before his move to Arles. Specifically, we examined Van Gogh’s use of first person singular pronouns (FPSP and first person plural pronouns (FPPP in the 415 letters he wrote while working as an artist before his move to Arles, and in the next 248 letters he wrote after his move to Arles until his death in Auvers-sur-Oise. During the latter period, Van Gogh’s use of FPSP showed an annual increase of 0.68% (SE = 0.15, p < 0.001 and his use of FPPP showed an annual decrease of 0.23% (SE = 0.04, p < 0.001, indicating increasing self-focus and vulnerability to depression. This trend differed from Van Gogh’s pronoun use in the former period (which showed no significant trend in FPSP, and an annual increase of FPPP of 0.03%, SE = 0.02, p = 0.04. This study suggests that Van Gogh’s death was preceded by a gradually increasing self-focus and vulnerability to depression. It also illustrates how existing methods (i.e., quantitative linguistic analysis and change-point analysis can be combined to study specific research questions in innovative ways.

  17. Paint it Black: Using Change-Point Analysis to Investigate Increasing Vulnerability to Depression towards the End of Vincent van Gogh’s Life

    Science.gov (United States)

    van Emmerik, Arnold A. P.; Hamaker, Ellen L.

    2017-01-01

    This study investigated whether Vincent van Gogh became increasingly self-focused—and thus vulnerable to depression—towards the end of his life, through a quantitative analysis of his written pronoun use over time. A change-point analysis was conducted on the time series formed by the pronoun use in Van Gogh’s letters. We used time as a predictor to see whether there was evidence for increased self-focus towards the end of Van Gogh’s life, and we compared this to the pattern in the letters written before his move to Arles. Specifically, we examined Van Gogh’s use of first person singular pronouns (FPSP) and first person plural pronouns (FPPP) in the 415 letters he wrote while working as an artist before his move to Arles, and in the next 248 letters he wrote after his move to Arles until his death in Auvers-sur-Oise. During the latter period, Van Gogh’s use of FPSP showed an annual increase of 0.68% (SE = 0.15, p depression. This trend differed from Van Gogh’s pronoun use in the former period (which showed no significant trend in FPSP, and an annual increase of FPPP of 0.03%, SE = 0.02, p = 0.04). This study suggests that Van Gogh’s death was preceded by a gradually increasing self-focus and vulnerability to depression. It also illustrates how existing methods (i.e., quantitative linguistic analysis and change-point analysis) can be combined to study specific research questions in innovative ways. PMID:28869542

  18. Paint it Black: Using Change-Point Analysis to Investigate Increasing Vulnerability to Depression towards the End of Vincent van Gogh's Life.

    Science.gov (United States)

    van Emmerik, Arnold A P; Hamaker, Ellen L

    2017-09-04

    This study investigated whether Vincent van Gogh became increasingly self-focused-and thus vulnerable to depression-towards the end of his life, through a quantitative analysis of his written pronoun use over time. A change-point analysis was conducted on the time series formed by the pronoun use in Van Gogh's letters. We used time as a predictor to see whether there was evidence for increased self-focus towards the end of Van Gogh's life, and we compared this to the pattern in the letters written before his move to Arles. Specifically, we examined Van Gogh's use of first person singular pronouns (FPSP) and first person plural pronouns (FPPP) in the 415 letters he wrote while working as an artist before his move to Arles, and in the next 248 letters he wrote after his move to Arles until his death in Auvers-sur-Oise. During the latter period, Van Gogh's use of FPSP showed an annual increase of 0.68% ( SE = 0.15, p depression. This trend differed from Van Gogh's pronoun use in the former period (which showed no significant trend in FPSP, and an annual increase of FPPP of 0.03%, SE = 0.02, p = 0.04). This study suggests that Van Gogh's death was preceded by a gradually increasing self-focus and vulnerability to depression. It also illustrates how existing methods (i.e., quantitative linguistic analysis and change-point analysis) can be combined to study specific research questions in innovative ways.

  19. Bayesian Model Averaging for Propensity Score Analysis

    Science.gov (United States)

    Kaplan, David; Chen, Jianshen

    2013-01-01

    The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…

  20. Bayesian methods for data analysis

    CERN Document Server

    Carlin, Bradley P.

    2009-01-01

    Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models The Bayes approach Introduction Prior Distributions Bayesian Inference Hierarchical Modeling Model Assessment Nonparametric Methods Bayesian computation Introduction Asymptotic Methods Noniterative Monte Carlo Methods Markov Chain Monte Carlo Methods Model criticism and selection Bayesian Modeling Bayesian Robustness Model Assessment Bayes Factors via Marginal Density Estimation Bayes Factors

  1. Bayesian Factor Analysis.

    Science.gov (United States)

    1985-03-23

    Fundamantal Factors of Comprehension in...8217’"" . " *. . . . • * • "• "". . . . " . . . . • . . # • • • . .° - -.... . ... .. . . . . . . . . ................. 1 ,,.,..,*, University of Iowa/Novick 8 March 1985 Dr. James McBride Program Manager for Manpower, Psychological...Princeton, NJ 08541 Dr. Vern W. Urry Dr. Peter Stoloff Personnel R&D Center Center for Naval Analysis Office of Personnel Management 200 North

  2. Bayesian analysis of longitudinal Johne's disease diagnostic data without a gold standard test

    DEFF Research Database (Denmark)

    Wang, C.; Turnbull, B.W.; Nielsen, Søren Saxmose

    2011-01-01

    A Bayesian methodology was developed based on a latent change-point model to evaluate the performance of milk ELISA and fecal culture tests for longitudinal Johne's disease diagnostic data. The situation of no perfect reference test was considered; that is, no “gold standard.” A change-point proc...... an area under the receiver operating characteristic curve (AUC) of 0.984, and is superior to the raw ELISA (AUC = 0.911) and fecal culture (sensitivity = 0.358, specificity = 0.980) tests for Johne's disease diagnosis....

  3. Bayesian analysis in plant pathology.

    Science.gov (United States)

    Mila, A L; Carriquiry, A L

    2004-09-01

    ABSTRACT Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.

  4. Bayesian change-point analyses in ecology

    Science.gov (United States)

    Brian Bekcage; Lawrence Joseph; Patrick Belisle; David B. Wolfson; William J. Platt

    2007-01-01

    Ecological and biological processes can change from one state to another once a threshold has been crossed in space or time. Threshold responses to incremental changes in underlying variables can characterize diverse processes from climate change to the desertification of arid lands from overgrazing.

  5. Implementing the Bayesian paradigm in risk analysis

    International Nuclear Information System (INIS)

    Aven, T.; Kvaloey, J.T.

    2002-01-01

    The Bayesian paradigm comprises a unified and consistent framework for analyzing and expressing risk. Yet, we see rather few examples of applications where the full Bayesian setting has been adopted with specifications of priors of unknown parameters. In this paper, we discuss some of the practical challenges of implementing Bayesian thinking and methods in risk analysis, emphasizing the introduction of probability models and parameters and associated uncertainty assessments. We conclude that there is a need for a pragmatic view in order to 'successfully' apply the Bayesian approach, such that we can do the assignments of some of the probabilities without adopting the somewhat sophisticated procedure of specifying prior distributions of parameters. A simple risk analysis example is presented to illustrate ideas

  6. Bayesian analysis for the social sciences

    CERN Document Server

    Jackman, Simon

    2009-01-01

    Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS - the most-widely used Bayesian analysis software in the world - and R - an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.

  7. Reliability analysis with Bayesian networks

    OpenAIRE

    Zwirglmaier, Kilian Martin

    2017-01-01

    Bayesian networks (BNs) represent a probabilistic modeling tool with large potential for reliability engineering. While BNs have been successfully applied to reliability engineering, there are remaining issues, some of which are addressed in this work. Firstly a classification of BN elicitation approaches is proposed. Secondly two approximate inference approaches, one of which is based on discretization and the other one on sampling, are proposed. These approaches are applicable to hybrid/con...

  8. Robust bayesian analysis of an autoregressive model with ...

    African Journals Online (AJOL)

    In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...

  9. Bayesian Meta-Analysis of Coefficient Alpha

    Science.gov (United States)

    Brannick, Michael T.; Zhang, Nanhua

    2013-01-01

    The current paper describes and illustrates a Bayesian approach to the meta-analysis of coefficient alpha. Alpha is the most commonly used estimate of the reliability or consistency (freedom from measurement error) for educational and psychological measures. The conventional approach to meta-analysis uses inverse variance weights to combine…

  10. Bayesian Nonparametric Longitudinal Data Analysis.

    Science.gov (United States)

    Quintana, Fernando A; Johnson, Wesley O; Waetjen, Elaine; Gold, Ellen

    2016-01-01

    Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.

  11. On Bayesian System Reliability Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Soerensen Ringi, M.

    1995-05-01

    The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person`s state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs.

  12. On Bayesian System Reliability Analysis

    International Nuclear Information System (INIS)

    Soerensen Ringi, M.

    1995-01-01

    The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person's state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs

  13. Detecting change-points in extremes

    KAUST Repository

    Dupuis, D. J.

    2015-01-01

    Even though most work on change-point estimation focuses on changes in the mean, changes in the variance or in the tail distribution can lead to more extreme events. In this paper, we develop a new method of detecting and estimating the change-points in the tail of multiple time series data. In addition, we adapt existing tail change-point detection methods to our specific problem and conduct a thorough comparison of different methods in terms of performance on the estimation of change-points and computational time. We also examine three locations on the U.S. northeast coast and demonstrate that the methods are useful for identifying changes in seasonally extreme warm temperatures.

  14. Combining morphological analysis and Bayesian networks for ...

    African Journals Online (AJOL)

    ... how these two computer aided methods may be combined to better facilitate modelling procedures. A simple example is presented, concerning a recent application in the field of environmental decision support. Keywords: Morphological analysis, Bayesian networks, strategic decision support. ORiON Vol. 23 (2) 2007: pp.

  15. Bayesian Correlation Analysis for Sequence Count Data.

    Directory of Open Access Journals (Sweden)

    Daniel Sánchez-Taltavull

    Full Text Available Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.

  16. Bayesian estimation and modeling: Editorial to the second special issue on Bayesian data analysis.

    Science.gov (United States)

    Chow, Sy-Miin; Hoijtink, Herbert

    2017-12-01

    This editorial accompanies the second special issue on Bayesian data analysis published in this journal. The emphases of this issue are on Bayesian estimation and modeling. In this editorial, we outline the basics of current Bayesian estimation techniques and some notable developments in the statistical literature, as well as adaptations and extensions by psychological researchers to better tailor to the modeling applications in psychology. We end with a discussion on future outlooks of Bayesian data analysis in psychology. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  17. A Gentle Introduction to Bayesian Analysis : Applications to Developmental Research

    NARCIS (Netherlands)

    Van de Schoot, Rens|info:eu-repo/dai/nl/304833207; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A G|info:eu-repo/dai/nl/081831218

    2014-01-01

    Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,

  18. A gentle introduction to Bayesian analysis : Applications to developmental research

    NARCIS (Netherlands)

    van de Schoot, R.; Kaplan, D.; Denissen, J.J.A.; Asendorpf, J.B.; Neyer, F.J.; van Aken, M.A.G.

    2014-01-01

    Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,

  19. Bayesian Inference in Statistical Analysis

    CERN Document Server

    Box, George E P

    2011-01-01

    The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson The Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences Rob

  20. Bayesian Analysis of Individual Level Personality Dynamics

    Directory of Open Access Journals (Sweden)

    Edward Cripps

    2016-07-01

    Full Text Available A Bayesian technique with analyses of within-person processes at the level of the individual is presented. The approach is used to examine if the patterns of within-person responses on a 12 trial simulation task are consistent with the predictions of ITA theory (Dweck, 1999. ITA theory states that the performance of an individual with an entity theory of ability is more likely to spiral down following a failure experience than the performance of an individual with an incremental theory of ability. This is because entity theorists interpret failure experiences as evidence of a lack of ability, which they believe is largely innate and therefore relatively fixed; whilst incremental theorists believe in the malleability of abilities and interpret failure experiences as evidence of more controllable factors such as poor strategy or lack of effort. The results of our analyses support ITA theory at both the within- and between-person levels of analyses and demonstrate the benefits of Bayesian techniques for the analysis of within-person processes. These include more formal specification of the theory and the ability to draw inferences about each individual, which allows for more nuanced interpretations of individuals within a personality category, such as differences in the individual probabilities of spiralling. While Bayesian techniques have many potential advantages for the analyses of within-person processes at the individual level, ease of use is not one of them for psychologists trained in traditional frequentist statistical techniques.

  1. On Bayesian Principal Component Analysis

    Czech Academy of Sciences Publication Activity Database

    Šmídl, Václav; Quinn, A.

    2007-01-01

    Roč. 51, č. 9 (2007), s. 4101-4123 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Principal component analysis ( PCA ) * Variational bayes (VB) * von-Mises–Fisher distribution Subject RIV: BC - Control Systems Theory Impact factor: 1.029, year: 2007 http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4MYD60N-6&_user=10&_coverDate=05%2F15%2F2007&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=b8ea629d48df926fe18f9e5724c9003a

  2. Bayesian data analysis tools for atomic physics

    Science.gov (United States)

    Trassinelli, Martino

    2017-10-01

    We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes' theorem and its applications. In particular we discuss about how to calculate simple and joint probability distributions and the Bayesian evidence, a model dependent quantity that allows to assign probabilities to different hypotheses from the analysis of a same data set. To give some practical examples, these methods are applied to two concrete cases. In the first example, the presence or not of a satellite line in an atomic spectrum is investigated. In the second example, we determine the most probable model among a set of possible profiles from the analysis of a statistically poor spectrum. We show also how to calculate the probability distribution of the main spectral component without having to determine uniquely the spectrum modeling. For these two studies, we implement the program Nested_fit to calculate the different probability distributions and other related quantities. Nested_fit is a Fortran90/Python code developed during the last years for analysis of atomic spectra. As indicated by the name, it is based on the nested algorithm, which is presented in details together with the program itself.

  3. Joint Bayesian analysis of forensic mixtures.

    Science.gov (United States)

    Pascali, Vince L; Merigioli, Sara

    2012-12-01

    Evaluation of series of PCR experiments referring to the same evidence is not infrequent in a forensic casework. This situation is met when 'series of results in mixture' (EPGs produced by reiterating PCR experiments over the same DNA mixture extract) have to be interpreted or when 'potentially related traces' (mixtures that can have contributors in common) require a combined interpretation. In these cases, there can be uncertainty on the genotype assignment, since: (a) more than one genotype combination fall under the same peak profile; (b) PCR preferential amplification alters pre-PCR allelic proportions; (c) other, more unpredictable technical problems (dropouts/dropins, etc.) take place. The uncertainty in the genotype assignment is in most cases addressed by empirical methods (selection of just one particular profile; extraction of consensual or composite profiles) that disregard part of the evidence. Genotype assignment should conversely take advantage from a joint Bayesian analysis (JBA) of all STRs peak areas generated at each experiment. This is the typical case of Bayesian analysis in which adoption of object-oriented Bayesian networks (OOBNs) could be highly helpful. Starting from experimentally designed mixtures, we created typical examples of 'series of results in mixture' of 'potentially related traces'. JBA was some administered to the whole peak area evidence, by specifically tailored OOBNs models, which enabled genotype assignment reflecting all the available evidence. Examples of a residual ambiguity in the genotype assignment came to light at assumed genotypes with partially overlapping alleles (for example: AB+AC→ABC). In the 'series of results in mixture', this uncertainty was in part refractory to the joint evaluation. Ambiguity was conversely dissipated at the 'potentially related' trace example, where the ABC allelic scheme at the first trace was interpreted together with other unambiguous combinations (ABCD; AB) at the related trace. We

  4. CHAMP: Changepoint Detection Using Approximate Model Parameters

    Science.gov (United States)

    2014-06-01

    form (with independent emissions or otherwise), in which parameter estimates are available via means such as maximum likelihood fit, MCMC , or sample ...counterparts, including the ability to generate a full posterior distribution over changepoint locations and offering a natural way to incorporate prior... sample consensus method. Our modifications also remove a significant restriction on model definition when detecting parameter changes within a single

  5. Bayesian Analysis of Bubbles in Asset Prices

    Directory of Open Access Journals (Sweden)

    Andras Fulop

    2017-10-01

    Full Text Available We develop a new model where the dynamic structure of the asset price, after the fundamental value is removed, is subject to two different regimes. One regime reflects the normal period where the asset price divided by the dividend is assumed to follow a mean-reverting process around a stochastic long run mean. The second regime reflects the bubble period with explosive behavior. Stochastic switches between two regimes and non-constant probabilities of exit from the bubble regime are both allowed. A Bayesian learning approach is employed to jointly estimate the latent states and the model parameters in real time. An important feature of our Bayesian method is that we are able to deal with parameter uncertainty and at the same time, to learn about the states and the parameters sequentially, allowing for real time model analysis. This feature is particularly useful for market surveillance. Analysis using simulated data reveals that our method has good power properties for detecting bubbles. Empirical analysis using price-dividend ratios of S&P500 highlights the advantages of our method.

  6. Doing bayesian data analysis a tutorial with R and BUGS

    CERN Document Server

    Kruschke, John K

    2011-01-01

    There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all

  7. Bayesian Model Averaging for Propensity Score Analysis.

    Science.gov (United States)

    Kaplan, David; Chen, Jianshen

    2014-01-01

    This article considers Bayesian model averaging as a means of addressing uncertainty in the selection of variables in the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA but that ignores uncertainty in the propensity score. We also provide a fully Bayesian model averaging approach via Markov chain Monte Carlo sampling (MCMC) to account for uncertainty in both parameters and models. A detailed study of our approach examines the differences in the causal estimate when incorporating noninformative versus informative priors in the model averaging stage. We examine these approaches under common methods of propensity score implementation. In addition, we evaluate the impact of changing the size of Occam's window used to narrow down the range of possible models. We also assess the predictive performance of both Bayesian model averaging propensity score approaches and compare it with the case without Bayesian model averaging. Overall, results show that both Bayesian model averaging propensity score approaches recover the treatment effect estimates well and generally provide larger uncertainty estimates, as expected. Both Bayesian model averaging approaches offer slightly better prediction of the propensity score compared with the Bayesian approach with a single propensity score equation. Covariate balance checks for the case study show that both Bayesian model averaging approaches offer good balance. The fully Bayesian model averaging approach also provides posterior probability intervals of the balance indices.

  8. Power in Bayesian Mediation Analysis for Small Sample Research

    NARCIS (Netherlands)

    Miočević, M.; MacKinnon, David; Levy, Roy

    2017-01-01

    Bayesian methods have the potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This article compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product,

  9. BEAST: Bayesian evolutionary analysis by sampling trees.

    Science.gov (United States)

    Drummond, Alexei J; Rambaut, Andrew

    2007-11-08

    The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license. BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.

  10. BEAST: Bayesian evolutionary analysis by sampling trees

    Directory of Open Access Journals (Sweden)

    Drummond Alexei J

    2007-11-01

    Full Text Available Abstract Background The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. Results BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license. Conclusion BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.

  11. Bayesian hypothesis testing: Editorial to the Special Issue on Bayesian data analysis.

    Science.gov (United States)

    Hoijtink, Herbert; Chow, Sy-Miin

    2017-06-01

    In the past 20 years, there has been a steadily increasing attention and demand for Bayesian data analysis across multiple scientific disciplines, including psychology. Bayesian methods and the related Markov chain Monte Carlo sampling techniques offered renewed ways of handling old and challenging new problems that may be difficult or impossible to handle using classical approaches. Yet, such opportunities and potential improvements have not been sufficiently explored and investigated. This is 1 of 2 special issues in Psychological Methods dedicated to the topic of Bayesian data analysis, with an emphasis on Bayesian hypothesis testing, model comparison, and general guidelines for applications in psychology. In this editorial, we provide an overview of the use of Bayesian methods in psychological research and a brief history of the Bayes factor and the posterior predictive p value. Translational abstracts that summarize the articles in this issue in very clear and understandable terms are included in the Appendix. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  12. Objective Bayesian Analysis of Skew- t Distributions

    KAUST Repository

    BRANCO, MARCIA D'ELIA

    2012-02-27

    We study the Jeffreys prior and its properties for the shape parameter of univariate skew-t distributions with linear and nonlinear Student\\'s t skewing functions. In both cases, we show that the resulting priors for the shape parameter are symmetric around zero and proper. Moreover, we propose a Student\\'s t approximation of the Jeffreys prior that makes an objective Bayesian analysis easy to perform. We carry out a Monte Carlo simulation study that demonstrates an overall better behaviour of the maximum a posteriori estimator compared with the maximum likelihood estimator. We also compare the frequentist coverage of the credible intervals based on the Jeffreys prior and its approximation and show that they are similar. We further discuss location-scale models under scale mixtures of skew-normal distributions and show some conditions for the existence of the posterior distribution and its moments. Finally, we present three numerical examples to illustrate the implications of our results on inference for skew-t distributions. © 2012 Board of the Foundation of the Scandinavian Journal of Statistics.

  13. Bayesian analysis of MEG visual evoked responses

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, D.M.; George, J.S.; Wood, C.C.

    1999-04-01

    The authors developed a method for analyzing neural electromagnetic data that allows probabilistic inferences to be drawn about regions of activation. The method involves the generation of a large number of possible solutions which both fir the data and prior expectations about the nature of probable solutions made explicit by a Bayesian formalism. In addition, they have introduced a model for the current distributions that produce MEG and (EEG) data that allows extended regions of activity, and can easily incorporate prior information such as anatomical constraints from MRI. To evaluate the feasibility and utility of the Bayesian approach with actual data, they analyzed MEG data from a visual evoked response experiment. They compared Bayesian analyses of MEG responses to visual stimuli in the left and right visual fields, in order to examine the sensitivity of the method to detect known features of human visual cortex organization. They also examined the changing pattern of cortical activation as a function of time.

  14. The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

    Science.gov (United States)

    Kruschke, John K; Liddell, Torrin M

    2018-02-01

    In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

  15. Adaptive bayesian analysis for binomial proportions

    CSIR Research Space (South Africa)

    Das, Sonali

    2008-10-01

    Full Text Available The authors consider the problem of statistical inference of binomial proportions for non-matched, correlated samples, under the Bayesian framework. Such inference can arise when the same group is observed at a different number of times with the aim...

  16. Bayesian methods in clinical trials: a Bayesian analysis of ECOG trials E1684 and E1690

    Directory of Open Access Journals (Sweden)

    Ibrahim Joseph G

    2012-11-01

    Full Text Available Abstract Background E1684 was the pivotal adjuvant melanoma trial for establishment of high-dose interferon (IFN as effective therapy of high-risk melanoma patients. E1690 was an intriguing effort to corroborate E1684, and the differences between the outcomes of these trials have embroiled the field in controversy over the past several years. The analyses of E1684 and E1690 were carried out separately when the results were published, and there were no further analyses trying to perform a single analysis of the combined trials. Method In this paper, we consider such a joint analysis by carrying out a Bayesian analysis of these two trials, thus providing us with a consistent and coherent methodology for combining the results from these two trials. Results The Bayesian analysis using power priors provided a more coherent flexible and potentially more accurate analysis than a separate analysis of these data or a frequentist analysis of these data. The methodology provides a consistent framework for carrying out a single unified analysis by combining data from two or more studies. Conclusions Such Bayesian analyses can be crucial in situations where the results from two theoretically identical trials yield somewhat conflicting or inconsistent results.

  17. Application of change-point analysis to determine winter sleep patterns of the raccoon dog (Nyctereutes procyonoides from body temperature recordings and a multi-faceted dietary and behavioral study of wintering

    Directory of Open Access Journals (Sweden)

    Mustonen Anne-Mari

    2012-12-01

    Full Text Available Abstract Background A multi-faceted approach was used to investigate the wintertime ecophysiology and behavioral patterns of the raccoon dog, Nyctereutes procyonoides, a suitable model for winter sleep studies. By utilizing GPS tracking, activity sensors, body temperature (Tb recordings, change-point analysis (CPA, home range, habitat and dietary analyses, as well as fatty acid signatures (FAS, the impact of the species on wintertime food webs was assessed. The timing of passive bouts was determined with multiple methods and compared to Tb data analyzed by CPA. Results Raccoon dogs displayed wintertime mobility, and the home range sizes determined by GPS were similar or larger than previous estimates by radio tracking. The preferred habitats were gardens, shores, deciduous forests, and sparsely forested areas. Fields had close to neutral preference; roads and railroads were utilized as travel routes. Raccoon dogs participated actively in the food web and gained benefit from human activity. Mammals, plants, birds, and discarded fish comprised the most important dietary classes, and the consumption of fish could be detected in FAS. Ambient temperature was an important external factor influencing Tb and activity. The timing of passive periods approximated by behavioral data and by CPA shared 91% similarity. Conclusions Passive periods can be determined with CPA from Tb recordings without the previously used time-consuming and expensive methods. It would be possible to recruit more animals by using the simple methods of data loggers and ear tags. Hunting could be used as a tool to return the ear-tagged individuals allowing the economical extension of follow-up studies. The Tb and CPA methods could be applied to other northern carnivores.

  18. Medical decision making tools: Bayesian analysis and ROC analysis

    International Nuclear Information System (INIS)

    Lee, Byung Do

    2006-01-01

    During the diagnostic process of the various oral and maxillofacial lesions, we should consider the following: 'When should we order diagnostic tests? What tests should be ordered? How should we interpret the results clinically? And how should we use this frequently imperfect information to make optimal medical decision?' For the clinicians to make proper judgement, several decision making tools are suggested. This article discusses the concept of the diagnostic accuracy (sensitivity and specificity values) with several decision making tools such as decision matrix, ROC analysis and Bayesian analysis. The article also explain the introductory concept of ORAD program

  19. Bayesian analysis of Markov point processes

    DEFF Research Database (Denmark)

    Berthelsen, Kasper Klitgaard; Møller, Jesper

    2006-01-01

    Recently Møller, Pettitt, Berthelsen and Reeves introduced a new MCMC methodology for drawing samples from a posterior distribution when the likelihood function is only specified up to a normalising constant. We illustrate the method in the setting of Bayesian inference for Markov point processes...... a partially ordered Markov point process as the auxiliary variable. As the method requires simulation from the "unknown" likelihood, perfect simulation algorithms for spatial point processes become useful....

  20. MATHEMATICAL RISK ANALYSIS: VIA NICHOLAS RISK MODEL AND BAYESIAN ANALYSIS

    Directory of Open Access Journals (Sweden)

    Anass BAYAGA

    2010-07-01

    Full Text Available The objective of this second part of a two-phased study was to explorethe predictive power of quantitative risk analysis (QRA method andprocess within Higher Education Institution (HEI. The method and process investigated the use impact analysis via Nicholas risk model and Bayesian analysis, with a sample of hundred (100 risk analysts in a historically black South African University in the greater Eastern Cape Province.The first findings supported and confirmed previous literature (KingIII report, 2009: Nicholas and Steyn, 2008: Stoney, 2007: COSA, 2004 that there was a direct relationship between risk factor, its likelihood and impact, certiris paribus. The second finding in relation to either controlling the likelihood or the impact of occurrence of risk (Nicholas risk model was that to have a brighter risk reward, it was important to control the likelihood ofoccurrence of risks as compared with its impact so to have a direct effect on entire University. On the Bayesian analysis, thus third finding, the impact of risk should be predicted along three aspects. These aspects included the human impact (decisions made, the property impact (students and infrastructural based and the business impact. Lastly, the study revealed that although in most business cases, where as business cycles considerably vary dependingon the industry and or the institution, this study revealed that, most impacts in HEI (University was within the period of one academic.The recommendation was that application of quantitative risk analysisshould be related to current legislative framework that affects HEI.

  1. Multilevel spatio-temporal dual changepoint models for relating alcohol outlet destruction and changes in neighbourhood rates of assaultive violence

    Directory of Open Access Journals (Sweden)

    Qingzhao Yu

    2008-05-01

    Full Text Available Many previous studies have suggested a link between alcohol outlets and assaultive violence. In this paper, we evaluate the impact of the “1992 Civil Unrest” in Los Angeles (which followed the “Rodney King incident”, in which many alcohol outlets were damaged leading to a decrease in alcohol outlet density, on crime. We leverage the natural experiment created by the closure of alcohol outlets in certain areas and not others to explore the effects of alcohol availability on assault crimes at the census tract level. We develop a hierarchical model that controls for important covariates (such as race and socio-economic status and accounts for unexplained spatial and temporal variability. While our model is somewhat complex, its hierarchical Bayesian analysis is accessible via the WinBUGS software. Our results show that, keeping other effects fixed, the reduction in alcohol availability within a census tract was associated with a drop in the assaultive violence rate at the census tract level. Comparing several dual candidate changepoint models using the Deviance Information Criterion, the drop in assaultive violence rate is best seen as having occurred one year after the reduction in alcohol availability, with the effect lasting roughly five years. We also create maps of the fitted assault rates in Los Angeles, as well as spatial residual maps that suggest various spatially-varying covariates are still missing from our model.

  2. Bayesian analysis of a correlated binomial model

    OpenAIRE

    Diniz, Carlos A. R.; Tutia, Marcelo H.; Leite, Jose G.

    2010-01-01

    In this paper a Bayesian approach is applied to the correlated binomial model, CB(n, p, ρ), proposed by Luceño (Comput. Statist. Data Anal. 20 (1995) 511–520). The data augmentation scheme is used in order to overcome the complexity of the mixture likelihood. MCMC methods, including Gibbs sampling and Metropolis within Gibbs, are applied to estimate the posterior marginal for the probability of success p and for the correlation coefficient ρ. The sensitivity of the posterior is studied taking...

  3. Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.

    Science.gov (United States)

    Jun, Sung C; George, John S; Paré-Blagoev, Juliana; Plis, Sergey M; Ranken, Doug M; Schmidt, David M; Wood, C C

    2005-10-15

    Recently, we described a Bayesian inference approach to the MEG/EEG inverse problem that used numerical techniques to estimate the full posterior probability distributions of likely solutions upon which all inferences were based [Schmidt, D.M., George, J.S., Wood, C.C., 1999. Bayesian inference applied to the electromagnetic inverse problem. Human Brain Mapping 7, 195; Schmidt, D.M., George, J.S., Ranken, D.M., Wood, C.C., 2001. Spatial-temporal bayesian inference for MEG/EEG. In: Nenonen, J., Ilmoniemi, R. J., Katila, T. (Eds.), Biomag 2000: 12th International Conference on Biomagnetism. Espoo, Norway, p. 671]. Schmidt et al. (1999) focused on the analysis of data at a single point in time employing an extended region source model. They subsequently extended their work to a spatiotemporal Bayesian inference analysis of the full spatiotemporal MEG/EEG data set. Here, we formulate spatiotemporal Bayesian inference analysis using a multi-dipole model of neural activity. This approach is faster than the extended region model, does not require use of the subject's anatomical information, does not require prior determination of the number of dipoles, and yields quantitative probabilistic inferences. In addition, we have incorporated the ability to handle much more complex and realistic estimates of the background noise, which may be represented as a sum of Kronecker products of temporal and spatial noise covariance components. This reduces the effects of undermodeling noise. In order to reduce the rigidity of the multi-dipole formulation which commonly causes problems due to multiple local minima, we treat the given covariance of the background as uncertain and marginalize over it in the analysis. Markov Chain Monte Carlo (MCMC) was used to sample the many possible likely solutions. The spatiotemporal Bayesian dipole analysis is demonstrated using simulated and empirical whole-head MEG data.

  4. Bayesian cost-effectiveness analysis with the R package BCEA

    CERN Document Server

    Baio, Gianluca; Heath, Anna

    2017-01-01

    The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach. Some relevant theory and introductory concepts are presented using practical examples and two running case studies. The book also describes in detail how to perform health economic evaluations using the R package BCEA (Bayesian Cost-Effectiveness Analysis). BCEA can be used to post-process the results of a Bayesian cost-effectiveness model and perform advanced analyses producing standardised and highly customisable outputs. It presents all the features of the package, including its many functions and their practical application, as well as its user-friendly web interface. The book is a valuable resource for statisticians and practitioners working in the field of health economics wanting to simplify and standardise their workflow, for example in the preparation of dossiers in support of marketing authorisation, or acade...

  5. The Application of Bayesian Spectral Analysis in Photometric Time Series

    Directory of Open Access Journals (Sweden)

    saeideh latif

    2017-11-01

    Full Text Available The present paper introduces the Bayesian spectral analysis as a powerful and efficient method for spectral analysis of photometric time series. For this purpose, Bayesian spectral analysis has programmed in Matlab software for XZ Dra photometric time series which is non-uniform with large gaps and the power spectrum of this analysis has compared with the power spectrum which obtained from the Period04 software, which designed for statistical analysis of astronomical time series and used of artificial data for unify the time series. Although in the power spectrum of this software, the main spectral peak which represent the main frequency of XZ Dra variable star oscillations in the f = 2.09864 (day -1 is well known but false spectral peaks are also seen. Also, in this software it’s not clear how to generate the synthetic data. These false peaks have been removed in the power spectrum which obtained from the Bayesian analysis; also this spectral peak which is around the desired frequency has a shorter width and is more accurate. It should be noted that in Bayesian spectral analysis, it’s not require to unify the time series for obtaining a desired power spectrum. Moreover, the researcher also becomes aware of the exact calculation process.

  6. A Bayesian Nonparametric Approach to Factor Analysis

    DEFF Research Database (Denmark)

    Piatek, Rémi; Papaspiliopoulos, Omiros

    2018-01-01

    This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does...... not impose any particular assumptions on the shape of the distribution of the factors, but still secures the basic requirements for the identification of the model. We design a new sampling scheme based on marginal data augmentation for the inference of mixtures of normals with location and scale...... restrictions. This approach is augmented by the use of a retrospective sampler, to allow for the inference of a constrained Dirichlet process mixture model for the distribution of the latent factors. We carry out a simulation study to illustrate the methodology and demonstrate its benefits. Our sampler is very...

  7. Research & development and growth: A Bayesian model averaging analysis

    Czech Academy of Sciences Publication Activity Database

    Horváth, Roman

    2011-01-01

    Roč. 28, č. 6 (2011), s. 2669-2673 ISSN 0264-9993. [Society for Non-linear Dynamics and Econometrics Annual Conferencen. Washington DC, 16.03.2011-18.03.2011] R&D Projects: GA ČR GA402/09/0965 Institutional research plan: CEZ:AV0Z10750506 Keywords : Research and development * Growth * Bayesian model averaging Subject RIV: AH - Economics Impact factor: 0.701, year: 2011 http://library.utia.cas.cz/separaty/2011/E/horvath-research & development and growth a bayesian model averaging analysis.pdf

  8. Stochastic back analysis of permeability coefficient using generalized Bayesian method

    Directory of Open Access Journals (Sweden)

    Zheng Guilan

    2008-09-01

    Full Text Available Owing to the fact that the conventional deterministic back analysis of the permeability coefficient cannot reflect the uncertainties of parameters, including the hydraulic head at the boundary, the permeability coefficient and measured hydraulic head, a stochastic back analysis taking consideration of uncertainties of parameters was performed using the generalized Bayesian method. Based on the stochastic finite element method (SFEM for a seepage field, the variable metric algorithm and the generalized Bayesian method, formulas for stochastic back analysis of the permeability coefficient were derived. A case study of seepage analysis of a sluice foundation was performed to illustrate the proposed method. The results indicate that, with the generalized Bayesian method that considers the uncertainties of measured hydraulic head, the permeability coefficient and the hydraulic head at the boundary, both the mean and standard deviation of the permeability coefficient can be obtained and the standard deviation is less than that obtained by the conventional Bayesian method. Therefore, the present method is valid and applicable.

  9. A new Bayesian Earthquake Analysis Tool (BEAT)

    Science.gov (United States)

    Vasyura-Bathke, Hannes; Dutta, Rishabh; Jónsson, Sigurjón; Mai, Martin

    2017-04-01

    Modern earthquake source estimation studies increasingly use non-linear optimization strategies to estimate kinematic rupture parameters, often considering geodetic and seismic data jointly. However, the optimization process is complex and consists of several steps that need to be followed in the earthquake parameter estimation procedure. These include pre-describing or modeling the fault geometry, calculating the Green's Functions (often assuming a layered elastic half-space), and estimating the distributed final slip and possibly other kinematic source parameters. Recently, Bayesian inference has become popular for estimating posterior distributions of earthquake source model parameters given measured/estimated/assumed data and model uncertainties. For instance, some research groups consider uncertainties of the layered medium and propagate these to the source parameter uncertainties. Other groups make use of informative priors to reduce the model parameter space. In addition, innovative sampling algorithms have been developed that efficiently explore the often high-dimensional parameter spaces. Compared to earlier studies, these improvements have resulted in overall more robust source model parameter estimates that include uncertainties. However, the computational demands of these methods are high and estimation codes are rarely distributed along with the published results. Even if codes are made available, it is often difficult to assemble them into a single optimization framework as they are typically coded in different programing languages. Therefore, further progress and future applications of these methods/codes are hampered, while reproducibility and validation of results has become essentially impossible. In the spirit of providing open-access and modular codes to facilitate progress and reproducible research in earthquake source estimations, we undertook the effort of producing BEAT, a python package that comprises all the above-mentioned features in one

  10. Bayesian analysis of stress thallium-201 scintigraphy

    International Nuclear Information System (INIS)

    The variation of the diagnostic value of stress T1-201 scintigraphy with prevalence of coronary heart disease (CHD) in the population has been investigated using Bayesian reasoning. From scintigraphic and arteriographic data obtained in 100 consecutive patients presenting with chest pain, the sensitivity of stress T1-201 scintigraphy for the detection of significant CHD was 90% and the specificity was 88%. From Bayes' Theorem, the posterior probability of having CHD for a given test result was calculated for prevalences of CHD ranging from 1% to 99%. the discriminant value of stress T1-201 scintigraphy was best when the prevalence of CHD lay between 30% and 70% and maximum for a prevalence of 52%. Thus, stress T1-201 scintigraphy would be an unsuitable diagnostic test where the prior probability of CHD is low, e.g., population screening programmes, and would add little where the clinical probability of having CHD is intermediate stress T1-201 scintigraphy may provide valuable diagnostic information. (orig.)

  11. Reliability demonstration test planning using bayesian analysis

    International Nuclear Information System (INIS)

    Chandran, Senthil Kumar; Arul, John A.

    2003-01-01

    In Nuclear Power Plants, the reliability of all the safety systems is very critical from the safety viewpoint and it is very essential that the required reliability requirements be met while satisfying the design constraints. From practical experience, it is found that the reliability of complex systems such as Safety Rod Drive Mechanism is of the order of 10 -4 with an uncertainty factor of 10. To demonstrate the reliability of such systems is prohibitive in terms of cost and time as the number of tests needed is very large. The purpose of this paper is to develop a Bayesian reliability demonstrating testing procedure for exponentially distributed failure times with gamma prior distribution on the failure rate which can be easily and effectively used to demonstrate component/subsystem/system reliability conformance to stated requirements. The important questions addressed in this paper are: With zero failures, how long one should perform the tests and how many components are required to conclude with a given degree of confidence, that the component under test, meets the reliability requirement. The procedure is explained with an example. This procedure can also be extended to demonstrate with more number of failures. The approach presented is applicable for deriving test plans for demonstrating component failure rates of nuclear power plants, as the failure data for similar components are becoming available in existing plants elsewhere. The advantages of this procedure are the criterion upon which the procedure is based is simple and pertinent, the fitting of the prior distribution is an integral part of the procedure and is based on the use of information regarding two percentiles of this distribution and finally, the procedure is straightforward and easy to apply in practice. (author)

  12. Extending the Functionality of Behavioural Change-Point Analysis with k-Means Clustering: A Case Study with the Little Penguin (Eudyptula minor).

    Science.gov (United States)

    Zhang, Jingjing; O'Reilly, Kathleen M; Perry, George L W; Taylor, Graeme A; Dennis, Todd E

    2015-01-01

    We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1) behavioural change point analysis to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3) k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known 'artificial behaviours' comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor) obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified.

  13. Extending the Functionality of Behavioural Change-Point Analysis with k-Means Clustering: A Case Study with the Little Penguin (Eudyptula minor.

    Directory of Open Access Journals (Sweden)

    Jingjing Zhang

    Full Text Available We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1 behavioural change point analysis to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2 hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3 k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known 'artificial behaviours' comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging, with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified.

  14. Operational modal analysis modeling, Bayesian inference, uncertainty laws

    CERN Document Server

    Au, Siu-Kui

    2017-01-01

    This book presents operational modal analysis (OMA), employing a coherent and comprehensive Bayesian framework for modal identification and covering stochastic modeling, theoretical formulations, computational algorithms, and practical applications. Mathematical similarities and philosophical differences between Bayesian and classical statistical approaches to system identification are discussed, allowing their mathematical tools to be shared and their results correctly interpreted. Many chapters can be used as lecture notes for the general topic they cover beyond the OMA context. After an introductory chapter (1), Chapters 2–7 present the general theory of stochastic modeling and analysis of ambient vibrations. Readers are first introduced to the spectral analysis of deterministic time series (2) and structural dynamics (3), which do not require the use of probability concepts. The concepts and techniques in these chapters are subsequently extended to a probabilistic context in Chapter 4 (on stochastic pro...

  15. An Overview of Bayesian Methods for Neural Spike Train Analysis

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  16. Bayesian phylogeny analysis via stochastic approximation Monte Carlo

    KAUST Repository

    Cheon, Sooyoung

    2009-11-01

    Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time. © 2009 Elsevier Inc. All rights reserved.

  17. Bayesian conformational analysis of ring molecules through reversible jump MCMC

    DEFF Research Database (Denmark)

    Nolsøe, Kim; Kessler, Mathieu; Pérez, José

    2005-01-01

    In this paper we address the problem of classifying the conformations of mmembered rings using experimental observations obtained by crystal structure analysis. We formulate a model for the data generation mechanism that consists in a multidimensional mixture model. We perform inference...... for the proportions and the components in a Bayesian framework, implementing an MCMC Reversible Jumps Algorithm to obtain samples of the posterior distributions. The method is illustrated on a simulated data set and on real data corresponding to cyclo-octane structures....

  18. A Bayesian Analysis of the Flood Frequency Hydrology Concept

    Science.gov (United States)

    2016-02-01

    ERDC/CHL CHETN-X-1 February 2016 Approved for public release; distribution is unlimited. A Bayesian Analysis of the Flood Frequency Hydrology ...flood frequency hydrology concept as a formal probabilistic-based means by which to coherently combine and also evaluate the worth of different types...and development. INTRODUCTION: Merz and Blöschl (2008a,b) proposed the concept of flood frequency hydrology , which emphasizes the importance of

  19. Bayesian Analysis Toolkit: 1.0 and beyond

    Science.gov (United States)

    Beaujean, Frederik; Caldwell, Allen; Greenwald, D.; Kluth, S.; Kröninger, Kevin; Schulz, O.

    2015-12-01

    The Bayesian Analysis Toolkit is a C++ package centered around Markov-chain Monte Carlo sampling. It is used in high-energy physics analyses by experimentalists and theorists alike. The software has matured over the last few years. We present new features to enter version 1.0, then summarize some of the software-engineering lessons learned and give an outlook on future versions.

  20. Bootstrapping a change-point Cox model for survival data

    Science.gov (United States)

    Xu, Gongjun; Sen, Bodhisattva; Ying, Zhiliang

    2014-01-01

    This paper investigates the (in)-consistency of various bootstrap methods for making inference on a change-point in time in the Cox model with right censored survival data. A criterion is established for the consistency of any bootstrap method. It is shown that the usual nonparametric bootstrap is inconsistent for the maximum partial likelihood estimation of the change-point. A new model-based bootstrap approach is proposed and its consistency established. Simulation studies are carried out to assess the performance of various bootstrap schemes. PMID:25400719

  1. Bayesian-network-based safety risk analysis in construction projects

    International Nuclear Information System (INIS)

    Zhang, Limao; Wu, Xianguo; Skibniewski, Miroslaw J.; Zhong, Jingbing; Lu, Yujie

    2014-01-01

    This paper presents a systemic decision support approach for safety risk analysis under uncertainty in tunnel construction. Fuzzy Bayesian Networks (FBN) is used to investigate causal relationships between tunnel-induced damage and its influential variables based upon the risk/hazard mechanism analysis. Aiming to overcome limitations on the current probability estimation, an expert confidence indicator is proposed to ensure the reliability of the surveyed data for fuzzy probability assessment of basic risk factors. A detailed fuzzy-based inference procedure is developed, which has a capacity of implementing deductive reasoning, sensitivity analysis and abductive reasoning. The “3σ criterion” is adopted to calculate the characteristic values of a triangular fuzzy number in the probability fuzzification process, and the α-weighted valuation method is adopted for defuzzification. The construction safety analysis progress is extended to the entire life cycle of risk-prone events, including the pre-accident, during-construction continuous and post-accident control. A typical hazard concerning the tunnel leakage in the construction of Wuhan Yangtze Metro Tunnel in China is presented as a case study, in order to verify the applicability of the proposed approach. The results demonstrate the feasibility of the proposed approach and its application potential. A comparison of advantages and disadvantages between FBN and fuzzy fault tree analysis (FFTA) as risk analysis tools is also conducted. The proposed approach can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment. - Highlights: • A systemic Bayesian network based approach for safety risk analysis is developed. • An expert confidence indicator for probability fuzzification is proposed. • Safety risk analysis progress is extended to entire life cycle of risk-prone events. • A typical

  2. Analysis of lifespan monitoring data using Bayesian logic

    International Nuclear Information System (INIS)

    Pozzi, M; Zonta, D; Glisic, B; Inaudi, D; Lau, J M; Fong, C C

    2011-01-01

    In this contribution, we use a Bayesian approach to analyze the data from a 19-storey building block, which is part of the Punggol EC26 construction project undertaken by the Singapore Housing and Development Board in the early 2000s. The building was instrumented during construction with interferometric fiber optic average strain sensors, embedded in ten of the first story columns during construction. The philosophy driving the design of the monitoring system was to instrument a representative number of structural elements, while maintaining the cost at a reasonable level. The analysis of the data, along with prior experience, allowed the engineer to recognize at early stage an ongoing differential settlement of one base column. We show how the whole cognitive process followed by the engineer can be reproduced using Bayesian logic. Particularly, we discuss to what extent the prior knowledge and potential evidence from inspection, can alter the perception of the building response based solely on instrumental data.

  3. A Bayesian on-off analysis of cosmic ray data

    Science.gov (United States)

    Nosek, Dalibor; Nosková, Jana

    2017-09-01

    We deal with the analysis of on-off measurements designed for the confirmation of a weak source of events whose presence is hypothesized, based on former observations. The problem of a small number of source events that are masked by an imprecisely known background is addressed from a Bayesian point of view. We examine three closely related variables, the posterior distributions of which carry relevant information about various aspects of the investigated phenomena. This information is utilized for predictions of further observations, given actual data. Backed by details of detection, we propose how to quantify disparities between different measurements. The usefulness of the Bayesian inference is demonstrated on examples taken from cosmic ray physics.

  4. Bayesian tomography and integrated data analysis in fusion diagnostics

    Science.gov (United States)

    Li, Dong; Dong, Y. B.; Deng, Wei; Shi, Z. B.; Fu, B. Z.; Gao, J. M.; Wang, T. B.; Zhou, Yan; Liu, Yi; Yang, Q. W.; Duan, X. R.

    2016-11-01

    In this article, a Bayesian tomography method using non-stationary Gaussian process for a prior has been introduced. The Bayesian formalism allows quantities which bear uncertainty to be expressed in the probabilistic form so that the uncertainty of a final solution can be fully resolved from the confidence interval of a posterior probability. Moreover, a consistency check of that solution can be performed by checking whether the misfits between predicted and measured data are reasonably within an assumed data error. In particular, the accuracy of reconstructions is significantly improved by using the non-stationary Gaussian process that can adapt to the varying smoothness of emission distribution. The implementation of this method to a soft X-ray diagnostics on HL-2A has been used to explore relevant physics in equilibrium and MHD instability modes. This project is carried out within a large size inference framework, aiming at an integrated analysis of heterogeneous diagnostics.

  5. A Bayesian Nonparametric Meta-Analysis Model

    Science.gov (United States)

    Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.

    2015-01-01

    In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…

  6. Bayesian networks for omics data analysis

    NARCIS (Netherlands)

    Gavai, A.K.

    2009-01-01

    This thesis focuses on two aspects of high throughput technologies, i.e. data storage and data analysis, in particular in transcriptomics and metabolomics. Both technologies are part of a research field that is generally called ‘omics’ (or ‘-omics’, with a leading hyphen), which refers to genomics,

  7. Robust Bayesian Analysis of Generalized Half Logistic Distribution

    Directory of Open Access Journals (Sweden)

    Ajit Chaturvedi

    2017-06-01

    Full Text Available In this paper, Robust Bayesian analysis of the generalized half logistic distribution (GHLD under an $\\epsilon$-contamination class of priors for the shape parameter $\\lambda$ is considered. ML-II Bayes estimators of the parameters, reliability function and hazard function are derived under the squared-error loss function (SELF and linear exponential (LINEX loss function by considering the Type~II censoring and the sampling scheme of Bartholomew (1963. Both the cases when scale parameter is known and unknown is considered under Type~II censoring and under the sampling scheme of Bartholomew. Simulation study and analysis of a real data set are presented.

  8. Prior Sensitivity Analysis in Default Bayesian Structural Equation Modeling.

    Science.gov (United States)

    van Erp, Sara; Mulder, Joris; Oberski, Daniel L

    2017-11-27

    Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models and solve some of the issues often encountered in classical maximum likelihood estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error, bias, coverage rates, and quantiles of the estimates. In this article, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors-with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature, and all code for conducting the prior sensitivity analysis is available in the online supplemental materials. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  9. Bayesian Sensitivity Analysis of Statistical Models with Missing Data.

    Science.gov (United States)

    Zhu, Hongtu; Ibrahim, Joseph G; Tang, Niansheng

    2014-04-01

    Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.

  10. A Bayesian analysis of pentaquark signals from CLAS data

    Energy Technology Data Exchange (ETDEWEB)

    David Ireland; Bryan McKinnon; Dan Protopopescu; Pawel Ambrozewicz; Marco Anghinolfi; G. Asryan; Harutyun Avakian; H. Bagdasaryan; Nathan Baillie; Jacques Ball; Nathan Baltzell; V. Batourine; Marco Battaglieri; Ivan Bedlinski; Ivan Bedlinskiy; Matthew Bellis; Nawal Benmouna; Barry Berman; Angela Biselli; Lukasz Blaszczyk; Sylvain Bouchigny; Sergey Boyarinov; Robert Bradford; Derek Branford; William Briscoe; William Brooks; Volker Burkert; Cornel Butuceanu; John Calarco; Sharon Careccia; Daniel Carman; Liam Casey; Shifeng Chen; Lu Cheng; Philip Cole; Patrick Collins; Philip Coltharp; Donald Crabb; Volker Crede; Natalya Dashyan; Rita De Masi; Raffaella De Vita; Enzo De Sanctis; Pavel Degtiarenko; Alexandre Deur; Richard Dickson; Chaden Djalali; Gail Dodge; Joseph Donnelly; David Doughty; Michael Dugger; Oleksandr Dzyubak; Hovanes Egiyan; Kim Egiyan; Lamiaa Elfassi; Latifa Elouadrhiri; Paul Eugenio; Gleb Fedotov; Gerald Feldman; Ahmed Fradi; Herbert Funsten; Michel Garcon; Gagik Gavalian; Nerses Gevorgyan; Gerard Gilfoyle; Kevin Giovanetti; Francois-Xavier Girod; John Goetz; Wesley Gohn; Atilla Gonenc; Ralf Gothe; Keith Griffioen; Michel Guidal; Nevzat Guler; Lei Guo; Vardan Gyurjyan; Kawtar Hafidi; Hayk Hakobyan; Charles Hanretty; Neil Hassall; F. Hersman; Ishaq Hleiqawi; Maurik Holtrop; Charles Hyde; Yordanka Ilieva; Boris Ishkhanov; Eugeny Isupov; D. Jenkins; Hyon-Suk Jo; John Johnstone; Kyungseon Joo; Henry Juengst; Narbe Kalantarians; James Kellie; Mahbubul Khandaker; Wooyoung Kim; Andreas Klein; Franz Klein; Mikhail Kossov; Zebulun Krahn; Laird Kramer; Valery Kubarovsky; Joachim Kuhn; Sergey Kuleshov; Viacheslav Kuznetsov; Jeff Lachniet; Jean Laget; Jorn Langheinrich; D. Lawrence; Kenneth Livingston; Haiyun Lu; Marion MacCormick; Nikolai Markov; Paul Mattione; Bernhard Mecking; Mac Mestayer; Curtis Meyer; Tsutomu Mibe; Konstantin Mikhaylov; Marco Mirazita; Rory Miskimen; Viktor Mokeev; Brahim Moreno; Kei Moriya; Steven Morrow; Maryam Moteabbed; Edwin Munevar Espitia; Gordon Mutchler; Pawel Nadel-Turonski; Rakhsha Nasseripour; Silvia Niccolai; Gabriel Niculescu; Maria-Ioana Niculescu; Bogdan Niczyporuk; Megh Niroula; Rustam Niyazov; Mina Nozar; Mikhail Osipenko; Alexander Ostrovidov; Kijun Park; Evgueni Pasyuk; Craig Paterson; Sergio Pereira; Joshua Pierce; Nikolay Pivnyuk; Oleg Pogorelko; Sergey Pozdnyakov; John Price; Sebastien Procureur; Yelena Prok; Brian Raue; Giovanni Ricco; Marco Ripani; Barry Ritchie; Federico Ronchetti; Guenther Rosner; Patrizia Rossi; Franck Sabatie; Julian Salamanca; Carlos Salgado; Joseph Santoro; Vladimir Sapunenko; Reinhard Schumacher; Vladimir Serov; Youri Sharabian; Dmitri Sharov; Nikolay Shvedunov; Elton Smith; Lee Smith; Daniel Sober; Daria Sokhan; Aleksey Stavinskiy; Samuel Stepanyan; Stepan Stepanyan; Burnham Stokes; Paul Stoler; Steffen Strauch; Mauro Taiuti; David Tedeschi; Ulrike Thoma; Avtandil Tkabladze; Svyatoslav Tkachenko; Clarisse Tur; Maurizio Ungaro; Michael Vineyard; Alexander Vlassov; Daniel Watts; Lawrence Weinstein; Dennis Weygand; M. Williams; Elliott Wolin; M.H. Wood; Amrit Yegneswaran; Lorenzo Zana; Jixie Zhang; Bo Zhao; Zhiwen Zhao

    2008-02-01

    We examine the results of two measurements by the CLAS collaboration, one of which claimed evidence for a $\\Theta^{+}$ pentaquark, whilst the other found no such evidence. The unique feature of these two experiments was that they were performed with the same experimental setup. Using a Bayesian analysis we find that the results of the two experiments are in fact compatible with each other, but that the first measurement did not contain sufficient information to determine unambiguously the existence of a $\\Theta^{+}$. Further, we suggest a means by which the existence of a new candidate particle can be tested in a rigorous manner.

  11. Bayesian analysis of log Gaussian Cox processes for disease mapping

    DEFF Research Database (Denmark)

    Benes, Viktor; Bodlák, Karel; Møller, Jesper

    We consider a data set of locations where people in Central Bohemia have been infected by tick-borne encephalitis, and where population census data and covariates concerning vegetation and altitude are available. The aims are to estimate the risk map of the disease and to study the dependence...... of the risk on the covariates. Instead of using the common area level approaches we consider a Bayesian analysis for a log Gaussian Cox point process with covariates. Posterior characteristics for a discretized version of the log Gaussian Cox process are computed using markov chain Monte Carlo methods...

  12. WebBUGS: Conducting Bayesian Statistical Analysis Online

    Directory of Open Access Journals (Sweden)

    Zhiyong Zhang

    2014-11-01

    Full Text Available A web interface, named WebBUGS, is developed to conduct Bayesian analysis online over the Internet through OpenBUGS and R. WebBUGS can be used with the minimum requirement of a web browser both remotely and locally. WebBUGS has many collaborative features such as email notification and sharing. WebBUGS also eases the use of OpenBUGS by providing built-in model templates, data management module, and other useful modules. In this paper, the use of WebBUGS is illustrated and discussed.

  13. Bayesian Reasoning in Data Analysis A Critical Introduction

    CERN Document Server

    D'Agostini, Giulio

    2003-01-01

    This book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional statistics") and its applications to data analysis. The basic ideas of this "new" approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and ar

  14. Implementation of a Bayesian Engine for Uncertainty Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Leng Vang; Curtis Smith; Steven Prescott

    2014-08-01

    In probabilistic risk assessment, it is important to have an environment where analysts have access to a shared and secured high performance computing and a statistical analysis tool package. As part of the advanced small modular reactor probabilistic risk analysis framework implementation, we have identified the need for advanced Bayesian computations. However, in order to make this technology available to non-specialists, there is also a need of a simplified tool that allows users to author models and evaluate them within this framework. As a proof-of-concept, we have implemented an advanced open source Bayesian inference tool, OpenBUGS, within the browser-based cloud risk analysis framework that is under development at the Idaho National Laboratory. This development, the “OpenBUGS Scripter” has been implemented as a client side, visual web-based and integrated development environment for creating OpenBUGS language scripts. It depends on the shared server environment to execute the generated scripts and to transmit results back to the user. The visual models are in the form of linked diagrams, from which we automatically create the applicable OpenBUGS script that matches the diagram. These diagrams can be saved locally or stored on the server environment to be shared with other users.

  15. Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models.

    Science.gov (United States)

    Hack, C Eric

    2006-04-17

    Physiologically based toxicokinetic (PBTK) and toxicodynamic (TD) models of bromate in animals and humans would improve our ability to accurately estimate the toxic doses in humans based on available animal studies. These mathematical models are often highly parameterized and must be calibrated in order for the model predictions of internal dose to adequately fit the experimentally measured doses. Highly parameterized models are difficult to calibrate and it is difficult to obtain accurate estimates of uncertainty or variability in model parameters with commonly used frequentist calibration methods, such as maximum likelihood estimation (MLE) or least squared error approaches. The Bayesian approach called Markov chain Monte Carlo (MCMC) analysis can be used to successfully calibrate these complex models. Prior knowledge about the biological system and associated model parameters is easily incorporated in this approach in the form of prior parameter distributions, and the distributions are refined or updated using experimental data to generate posterior distributions of parameter estimates. The goal of this paper is to give the non-mathematician a brief description of the Bayesian approach and Markov chain Monte Carlo analysis, how this technique is used in risk assessment, and the issues associated with this approach.

  16. DATMAN: A reliability data analysis program using Bayesian updating

    International Nuclear Information System (INIS)

    Becker, M.; Feltus, M.A.

    1996-01-01

    Preventive maintenance (PM) techniques focus on the prevention of failures, in particular, system components that are important to plant functions. Reliability-centered maintenance (RCM) improves on the PM techniques by introducing a set of guidelines by which to evaluate the system functions. It also minimizes intrusive maintenance, labor, and equipment downtime without sacrificing system performance when its function is essential for plant safety. Both the PM and RCM approaches require that system reliability data be updated as more component failures and operation time are acquired. Systems reliability and the likelihood of component failures can be calculated by Bayesian statistical methods, which can update these data. The DATMAN computer code has been developed at Penn State to simplify the Bayesian analysis by performing tedious calculations needed for RCM reliability analysis. DATMAN reads data for updating, fits a distribution that best fits the data, and calculates component reliability. DATMAN provides a user-friendly interface menu that allows the user to choose from several common prior and posterior distributions, insert new failure data, and visually select the distribution that matches the data most accurately

  17. Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm

    Directory of Open Access Journals (Sweden)

    Fengkai Yang

    2014-01-01

    normal distribution, we developed the expectation maximization (EM algorithm to estimate the position of mean change-point. We investigated the performance of the algorithm through different simulations, finding that our methods is robust to the distributions of errors and is effective to estimate the position of mean change-point. Finally, we applied our method to the classical Holbert data and detected a change-point.

  18. Bayesian data analysis of the dynamics of rolling leukocytes

    Science.gov (United States)

    Moskopp, Mats Leif; Preuss, Roland; Deussen, Andreas; Chavakis, Triantafyllos; Dieterich, Peter

    2013-08-01

    The coordinated recruitment of leukocytes to sites of infection and inflammation is a central process of the immune system and proceeds in several steps. Here we focus on the dynamics of rolling leukocytes obtained from in vitro experiments. Trajectories of rolling leukocytes in small flow chambers are acquired with phase contrast microscopy under different levels of fluid shear stress and a variation of protein coatings of the (adhesive) surfaces. Bayesian data analysis of a random walk model including drift is applied to individual trajectories of leukocytes. The analysis allows the estimation of drift velocities and diffusion coefficients within an uncertainty of about 10% and shows a certain homogeneity of the cell groups. Drift velocities of cells saturate in spite of increasing fluid flow. In addition, the analysis reveals some correlated fluctuations of cells' translocations requiring a refinement of the stochastic model.

  19. Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis

    International Nuclear Information System (INIS)

    Simon, C.; Weber, P.; Evsukoff, A.

    2008-01-01

    This paper deals with the use of Bayesian networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian networks are identified. The basic concepts of the Bayesian networks application to reliability analysis are introduced and a model to compute the reliability for the case study is presented. Dempster Shafer theory to treat epistemic uncertainty in reliability analysis is then discussed and its basic concepts that can be applied thanks to the Bayesian network inference algorithm are introduced. Finally, it is shown, with a numerical example, how Bayesian networks' inference algorithms compute complex system reliability and what the Dempster Shafer theory can provide to reliability analysis

  20. BATMAN: Bayesian Technique for Multi-image Analysis

    Science.gov (United States)

    Casado, J.; Ascasibar, Y.; García-Benito, R.; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, S. F.; Díaz, A. I.

    2017-04-01

    This paper describes the Bayesian Technique for Multi-image Analysis (BATMAN), a novel image-segmentation technique based on Bayesian statistics that characterizes any astronomical data set containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (I.e. identical signal within the errors). We illustrate its operation and performance with a set of test cases including both synthetic and real integral-field spectroscopic data. The output segmentations adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. The quality of the recovered signal represents an improvement with respect to the input, especially in regions with low signal-to-noise ratio. However, the algorithm may be sensitive to small-scale random fluctuations, and its performance in presence of spatial gradients is limited. Due to these effects, errors may be underestimated by as much as a factor of 2. Our analysis reveals that the algorithm prioritizes conservation of all the statistically significant information over noise reduction, and that the precise choice of the input data has a crucial impact on the results. Hence, the philosophy of BaTMAn is not to be used as a 'black box' to improve the signal-to-noise ratio, but as a new approach to characterize spatially resolved data prior to its analysis. The source code is publicly available at http://astro.ft.uam.es/SELGIFS/BaTMAn.

  1. A Bayesian Analysis of Unobserved Component Models Using Ox

    Directory of Open Access Journals (Sweden)

    Charles S. Bos

    2011-05-01

    Full Text Available This article details a Bayesian analysis of the Nile river flow data, using a similar state space model as other articles in this volume. For this data set, Metropolis-Hastings and Gibbs sampling algorithms are implemented in the programming language Ox. These Markov chain Monte Carlo methods only provide output conditioned upon the full data set. For filtered output, conditioning only on past observations, the particle filter is introduced. The sampling methods are flexible, and this advantage is used to extend the model to incorporate a stochastic volatility process. The volatility changes both in the Nile data and also in daily S&P 500 return data are investigated. The posterior density of parameters and states is found to provide information on which elements of the model are easily identifiable, and which elements are estimated with less precision.

  2. Bayesian analysis of factors associated with fibromyalgia syndrome subjects

    Science.gov (United States)

    Jayawardana, Veroni; Mondal, Sumona; Russek, Leslie

    2015-01-01

    Factors contributing to movement-related fear were assessed by Russek, et al. 2014 for subjects with Fibromyalgia (FM) based on the collected data by a national internet survey of community-based individuals. The study focused on the variables, Activities-Specific Balance Confidence scale (ABC), Primary Care Post-Traumatic Stress Disorder screen (PC-PTSD), Tampa Scale of Kinesiophobia (TSK), a Joint Hypermobility Syndrome screen (JHS), Vertigo Symptom Scale (VSS-SF), Obsessive-Compulsive Personality Disorder (OCPD), Pain, work status and physical activity dependent from the "Revised Fibromyalgia Impact Questionnaire" (FIQR). The study presented in this paper revisits same data with a Bayesian analysis where appropriate priors were introduced for variables selected in the Russek's paper.

  3. Bayesian analysis for uncertainty estimation of a canopy transpiration model

    Science.gov (United States)

    Samanta, S.; Mackay, D. S.; Clayton, M. K.; Kruger, E. L.; Ewers, B. E.

    2007-04-01

    A Bayesian approach was used to fit a conceptual transpiration model to half-hourly transpiration rates for a sugar maple (Acer saccharum) stand collected over a 5-month period and probabilistically estimate its parameter and prediction uncertainties. The model used the Penman-Monteith equation with the Jarvis model for canopy conductance. This deterministic model was extended by adding a normally distributed error term. This extension enabled using Markov chain Monte Carlo simulations to sample the posterior parameter distributions. The residuals revealed approximate conformance to the assumption of normally distributed errors. However, minor systematic structures in the residuals at fine timescales suggested model changes that would potentially improve the modeling of transpiration. Results also indicated considerable uncertainties in the parameter and transpiration estimates. This simple methodology of uncertainty analysis would facilitate the deductive step during the development cycle of deterministic conceptual models by accounting for these uncertainties while drawing inferences from data.

  4. Bayesian nonparametric meta-analysis using Polya tree mixture models.

    Science.gov (United States)

    Branscum, Adam J; Hanson, Timothy E

    2008-09-01

    Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.

  5. Spatial Dependence and Heterogeneity in Bayesian Factor Analysis : A Cross-National Investigation of Schwartz Values

    NARCIS (Netherlands)

    Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel

    2012-01-01

    In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the

  6. Piecewise mixed-effects models with skew distributions for evaluating viral load changes: A Bayesian approach.

    Science.gov (United States)

    Huang, Yangxin; Dagne, Getachew A; Zhou, Shumin; Wang, Zhongjun

    2015-12-01

    Studies of human immunodeficiency virus dynamics in acquired immuno deficiency syndrome (AIDS) research are very important in evaluating the effectiveness of antiretroviral (ARV) therapies. The potency of ARV agents in AIDS clinical trials can be assessed on the basis of a viral response such as viral decay rate or viral load change in plasma. Following ARV treatment, the profile of each subject's viral load tends to follow a 'broken stick'-like dynamic trajectory, indicating multiple phases of decline and increase in viral loads. Such multiple-phases (change-points) can be described by a random change-point model with random subject-specific parameters. One usually assumes a normal distribution for model error. However, this assumption may be unrealistic, obscuring important features of within- and among-subject variations. In this article, we propose piecewise linear mixed-effects models with skew-elliptical distributions to describe the time trend of a response variable under a Bayesian framework. This methodology can be widely applied to real problems for longitudinal studies. A real data analysis, using viral load data from an AIDS study, is carried out to illustrate the proposed method by comparing various candidate models. Biologically important findings are reported, and these findings also suggest that it is very important to assume a model with skew distribution in order to achieve reliable results, in particular, when the data exhibit skewness. © The Author(s) 2011.

  7. Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

    Science.gov (United States)

    Dezfuli, Homayoon; Kelly, Dana; Smith, Curtis; Vedros, Kurt; Galyean, William

    2009-01-01

    This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

  8. Bayesian multivariate meta-analysis of multiple factors.

    Science.gov (United States)

    Lin, Lifeng; Chu, Haitao

    2018-02-09

    In medical sciences, a disease condition is typically associated with multiple risk and protective factors. Although many studies report results of multiple factors, nearly all meta-analyses separately synthesize the association between each factor and the disease condition of interest. The collected studies usually report different subsets of factors, and the results from separate analyses on multiple factors may not be comparable because each analysis may use different subpopulation. This may impact on selecting most important factors to design a multifactor intervention program. This article proposes a new concept, multivariate meta-analysis of multiple factors (MVMA-MF), to synthesize all available factors simultaneously. By borrowing information across factors, MVMA-MF can improve statistical efficiency and reduce biases compared with separate analyses when factors were missing not at random. As within-study correlations between factors are commonly unavailable from published articles, we use a Bayesian hybrid model to perform MVMA-MF, which effectively accounts for both within- and between-study correlations. The performance of MVMA-MF and the conventional methods are compared using simulations and an application to a pterygium dataset consisting of 29 studies on 8 risk factors. Copyright © 2018 John Wiley & Sons, Ltd.

  9. Thermodynamically consistent Bayesian analysis of closed biochemical reaction systems

    Directory of Open Access Journals (Sweden)

    Goutsias John

    2010-11-01

    Full Text Available Abstract Background Estimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable. Results We introduce a Bayesian analysis approach for computing thermodynamically consistent estimates of the rate constants of a closed biochemical reaction system with known stoichiometry given experimental data. Our method employs an appropriately designed prior probability density function that effectively integrates fundamental biophysical and thermodynamic knowledge into the inference problem. Moreover, it takes into account experimental strategies for collecting informative observations of molecular concentrations through perturbations. The proposed method employs a maximization-expectation-maximization algorithm that provides thermodynamically feasible estimates of the rate constant values and computes appropriate measures of estimation accuracy. We demonstrate various aspects of the proposed method on synthetic data obtained by simulating a subset of a well-known model of the EGF/ERK signaling pathway, and examine its robustness under conditions that violate key assumptions. Software, coded in MATLAB®, which implements all Bayesian analysis techniques discussed in this paper, is available free of charge at http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.html. Conclusions Our approach provides an attractive statistical methodology for

  10. Prior sensitivity analysis in default Bayesian structural equation modeling

    NARCIS (Netherlands)

    van Erp, S.J.; Mulder, J.; Oberski, Daniel L.

    2018-01-01

    Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models while solving some of the issues often encountered in classical maximum likelihood (ML) estimation, such as nonconvergence and inadmissible solutions. An important

  11. Bayesian uncertainty analysis with applications to turbulence modeling

    International Nuclear Information System (INIS)

    Cheung, Sai Hung; Oliver, Todd A.; Prudencio, Ernesto E.; Prudhomme, Serge; Moser, Robert D.

    2011-01-01

    In this paper, we apply Bayesian uncertainty quantification techniques to the processes of calibrating complex mathematical models and predicting quantities of interest (QoI's) with such models. These techniques also enable the systematic comparison of competing model classes. The processes of calibration and comparison constitute the building blocks of a larger validation process, the goal of which is to accept or reject a given mathematical model for the prediction of a particular QoI for a particular scenario. In this work, we take the first step in this process by applying the methodology to the analysis of the Spalart-Allmaras turbulence model in the context of incompressible, boundary layer flows. Three competing model classes based on the Spalart-Allmaras model are formulated, calibrated against experimental data, and used to issue a prediction with quantified uncertainty. The model classes are compared in terms of their posterior probabilities and their prediction of QoI's. The model posterior probability represents the relative plausibility of a model class given the data. Thus, it incorporates the model's ability to fit experimental observations. Alternatively, comparing models using the predicted QoI connects the process to the needs of decision makers that use the results of the model. We show that by using both the model plausibility and predicted QoI, one has the opportunity to reject some model classes after calibration, before subjecting the remaining classes to additional validation challenges.

  12. Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

    Science.gov (United States)

    Scargle, Jeffrey D.; Norris, Jay P.; Jackson, Brad; Chiang, James

    2013-01-01

    This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it-an improved and generalized version of Bayesian Blocks [Scargle 1998]-that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piece- wise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by [Arias-Castro, Donoho and Huo 2003]. In the spirit of Reproducible Research [Donoho et al. (2008)] all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.

  13. Combining morphological analysis and Bayesian networks for strategic decision support

    Directory of Open Access Journals (Sweden)

    A de Waal

    2007-12-01

    Full Text Available Morphological analysis (MA and Bayesian networks (BN are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of a qualitative and quantitative part. The qualitative part is a cause-and-effect, or causal graph. The quantitative part depicts the strength of the causal relationships between variables. Combining MA and BN, as two phases in a modelling process, allows us to gain the benefits of both of these methods. The strength of MA lies in defining, linking and internally evaluating the parameters of problem spaces and BN modelling allows for the definition and quantification of causal relationships between variables. Short summaries of MA and BN are provided in this paper, followed by discussions how these two computer aided methods may be combined to better facilitate modelling procedures. A simple example is presented, concerning a recent application in the field of environmental decision support.

  14. Bayesian analysis of a reduced-form air quality model.

    Science.gov (United States)

    Foley, Kristen M; Reich, Brian J; Napelenok, Sergey L

    2012-07-17

    Numerical air quality models are being used for assessing emission control strategies for improving ambient pollution levels across the globe. This paper applies probabilistic modeling to evaluate the effectiveness of emission reduction scenarios aimed at lowering ground-level ozone concentrations. A Bayesian hierarchical model is used to combine air quality model output and monitoring data in order to characterize the impact of emissions reductions while accounting for different degrees of uncertainty in the modeled emissions inputs. The probabilistic model predictions are weighted based on population density in order to better quantify the societal benefits/disbenefits of four hypothetical emission reduction scenarios in which domain-wide NO(x) emissions from various sectors are reduced individually and then simultaneously. Cross validation analysis shows the statistical model performs well compared to observed ozone levels. Accounting for the variability and uncertainty in the emissions and atmospheric systems being modeled is shown to impact how emission reduction scenarios would be ranked, compared to standard methodology.

  15. Bayesian analysis of inflation: Parameter estimation for single field models

    International Nuclear Information System (INIS)

    Mortonson, Michael J.; Peiris, Hiranya V.; Easther, Richard

    2011-01-01

    Future astrophysical data sets promise to strengthen constraints on models of inflation, and extracting these constraints requires methods and tools commensurate with the quality of the data. In this paper we describe ModeCode, a new, publicly available code that computes the primordial scalar and tensor power spectra for single-field inflationary models. ModeCode solves the inflationary mode equations numerically, avoiding the slow roll approximation. It is interfaced with CAMB and CosmoMC to compute cosmic microwave background angular power spectra and perform likelihood analysis and parameter estimation. ModeCode is easily extendable to additional models of inflation, and future updates will include Bayesian model comparison. Errors from ModeCode contribute negligibly to the error budget for analyses of data from Planck or other next generation experiments. We constrain representative single-field models (φ n with n=2/3, 1, 2, and 4, natural inflation, and 'hilltop' inflation) using current data, and provide forecasts for Planck. From current data, we obtain weak but nontrivial limits on the post-inflationary physics, which is a significant source of uncertainty in the predictions of inflationary models, while we find that Planck will dramatically improve these constraints. In particular, Planck will link the inflationary dynamics with the post-inflationary growth of the horizon, and thus begin to probe the ''primordial dark ages'' between TeV and grand unified theory scale energies.

  16. Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

    Science.gov (United States)

    Scargle, Jeffrey D.; Norris, Jay P.; Jackson, Brad; Chiang, James

    2013-02-01

    This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it—an improved and generalized version of Bayesian Blocks—that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piecewise linear and piecewise exponential representations, multivariate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by Arias-Castro et al. In the spirit of Reproducible Research all of the code and data necessary to reproduce all of the figures in this paper are included as supplementary material.

  17. Binary naive Bayesian classifiers for correlated Gaussian features: a theoretical analysis

    CSIR Research Space (South Africa)

    Van Dyk, E

    2008-11-01

    Full Text Available We investigate the use of Naive Bayesian classifiers for correlated Gaussian feature spaces and derive error estimates for these classifiers. The error analysis is done by developing an exact expression for the error performance of a binary...

  18. Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods

    NARCIS (Netherlands)

    A. Zellner (Arnold); L. Bauwens (Luc); H.K. van Dijk (Herman)

    1988-01-01

    textabstractBayesian procedures for specification analysis or diagnostic checking of modeling assumptions for structural equations of econometric models are developed and applied using Monte Carlo numerical methods. Checks on the validity of identifying restrictions, exogeneity assumptions and other

  19. Sequential Change-Point Detection via Online Convex Optimization

    Directory of Open Access Journals (Sweden)

    Yang Cao

    2018-02-01

    Full Text Available Sequential change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. When the post-change parameters are unknown, we consider a set of detection procedures based on sequential likelihood ratios with non-anticipating estimators constructed using online convex optimization algorithms such as online mirror descent, which provides a more versatile approach to tackling complex situations where recursive maximum likelihood estimators cannot be found. When the underlying distributions belong to a exponential family and the estimators satisfy the logarithm regret property, we show that this approach is nearly second-order asymptotically optimal. This means that the upper bound for the false alarm rate of the algorithm (measured by the average-run-length meets the lower bound asymptotically up to a log-log factor when the threshold tends to infinity. Our proof is achieved by making a connection between sequential change-point and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm. Numerical and real data examples validate our theory.

  20. Evolutionary Sequential Monte Carlo Samplers for Change-Point Models

    Directory of Open Access Journals (Sweden)

    Arnaud Dufays

    2016-03-01

    Full Text Available Sequential Monte Carlo (SMC methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC methods. Not only do SMC algorithms draw posterior distributions of static or dynamic parameters but additionally they provide an estimate of the marginal likelihood. The tempered and time (TNT algorithm, developed in this paper, combines (off-line tempered SMC inference with on-line SMC inference for drawing realizations from many sequential posterior distributions without experiencing a particle degeneracy problem. Furthermore, it introduces a new MCMC rejuvenation step that is generic, automated and well-suited for multi-modal distributions. As this update relies on the wide heuristic optimization literature, numerous extensions are readily available. The algorithm is notably appropriate for estimating change-point models. As an example, we compare several change-point GARCH models through their marginal log-likelihoods over time.

  1. Review of bayesian statistical analysis methods for cytogenetic radiation biodosimetry, with a practical example

    International Nuclear Information System (INIS)

    Ainsbury, Elizabeth A.; Lloyd, David C.; Rothkamm, Kai; Vinnikov, Volodymyr A.; Maznyk, Nataliya A.; Puig, Pedro; Higueras, Manuel

    2014-01-01

    Classical methods of assessing the uncertainty associated with radiation doses estimated using cytogenetic techniques are now extremely well defined. However, several authors have suggested that a Bayesian approach to uncertainty estimation may be more suitable for cytogenetic data, which are inherently stochastic in nature. The Bayesian analysis framework focuses on identification of probability distributions (for yield of aberrations or estimated dose), which also means that uncertainty is an intrinsic part of the analysis, rather than an 'afterthought'. In this paper Bayesian, as well as some more advanced classical, data analysis methods for radiation cytogenetics are reviewed that have been proposed in the literature. A practical overview of Bayesian cytogenetic dose estimation is also presented, with worked examples from the literature. (authors)

  2. An Automated Bayesian Framework for Integrative Gene Expression Analysis and Predictive Medicine

    OpenAIRE

    Parikh, Neena; Zollanvari, Amin; Alterovitz, Gil

    2012-01-01

    Motivation: This work constructs a closed loop Bayesian Network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases. Results: An automated pipeline was successfully constructed. Integrative models were made based on gene expression data obtained from GEO experiments relating to four different diseases using Bayesian statistical methods. Many of these models demonstrated a high level of accuracy and predictive...

  3. Bayesian analysis of right censored survival time data | Abiodun ...

    African Journals Online (AJOL)

    We analyzed cancer data using Fully Bayesian inference approach based on Markov Chain Monte Carlo (MCMC) simulation technique which allows the estimation of very complex and realistic models. The results show that sex and age are significant risk factors for dying from some selected cancers. The risk of dying from ...

  4. Exploiting sensitivity analysis in Bayesian networks for consumer satisfaction study

    NARCIS (Netherlands)

    Jaronski, W.; Bloemer, J.M.M.; Vanhoof, K.; Wets, G.

    2004-01-01

    The paper presents an application of Bayesian network technology in a empirical customer satisfaction study. The findings of the study should provide insight as to the importance of product/service dimensions in terms of the strength of their influence on overall satisfaction. To this end we apply a

  5. Review of applications of Bayesian meta-analysis in systematic reviews

    Directory of Open Access Journals (Sweden)

    Melissa Glenda Lewis

    2015-01-01

    Full Text Available Background: Systematic reviews are important sources of evidence in health care research. These reviews may or may not include meta-analysis as a statistical assimilation of the results of several studies in order to acquire a pooled estimate. Systematic review with meta-analysis is considered as a robust method of evidence synthesis. The methodology concerned with traditional meta-analysis does not incorporate external prior information. Hence, Bayesian methods are essential due to the natural process of incorporating the past information and updating the belief. Bayesian methods to meta-analysis have been developed with a motivation from the limitations of traditional meta-analysis such as dealing with missing data, problem with limited number of studies and problem with sparse event data in both the groups. The present article aims to unearth as to what extent Bayesian methods have been used in systematic reviews, evolution and its applications. This article also highlights the existing challenges and opportunities. Methods: The literature search was performed in databases such as Cochrane, PubMed, ProQuest and Scopus using the keywords “Bayesian Meta-analysis” and “Bayesian Meta-analyses”. All the methodology and application oriented papers specific to Bayesian meta-analysis were considered relevant for this review. Conclusion: Bayesian meta-analysis has gained popularity in the field of evidence synthesis of clinical trials. However, it did not pick up momentum in summarizing public health interventions, owing to the fact that public health interventions are targeted to highly heterogeneous population, multi-component interventions, and multiple outcomes and influenced by the context

  6. RADYBAN: A tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks

    International Nuclear Information System (INIS)

    Montani, S.; Portinale, L.; Bobbio, A.; Codetta-Raiteri, D.

    2008-01-01

    In this paper, we present RADYBAN (Reliability Analysis with DYnamic BAyesian Networks), a software tool which allows to analyze a dynamic fault tree relying on its conversion into a dynamic Bayesian network. The tool implements a modular algorithm for automatically translating a dynamic fault tree into the corresponding dynamic Bayesian network and exploits classical algorithms for the inference on dynamic Bayesian networks, in order to compute reliability measures. After having described the basic features of the tool, we show how it operates on a real world example and we compare the unreliability results it generates with those returned by other methodologies, in order to verify the correctness and the consistency of the results obtained

  7. A Bayesian approach to meta-analysis of plant pathology studies.

    Science.gov (United States)

    Mila, A L; Ngugi, H K

    2011-01-01

    Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework

  8. Conjunction analysis and propositional logic in fMRI data analysis using Bayesian statistics.

    Science.gov (United States)

    Rudert, Thomas; Lohmann, Gabriele

    2008-12-01

    To evaluate logical expressions over different effects in data analyses using the general linear model (GLM) and to evaluate logical expressions over different posterior probability maps (PPMs). In functional magnetic resonance imaging (fMRI) data analysis, the GLM was applied to estimate unknown regression parameters. Based on the GLM, Bayesian statistics can be used to determine the probability of conjunction, disjunction, implication, or any other arbitrary logical expression over different effects or contrast. For second-level inferences, PPMs from individual sessions or subjects are utilized. These PPMs can be combined to a logical expression and its probability can be computed. The methods proposed in this article are applied to data from a STROOP experiment and the methods are compared to conjunction analysis approaches for test-statistics. The combination of Bayesian statistics with propositional logic provides a new approach for data analyses in fMRI. Two different methods are introduced for propositional logic: the first for analyses using the GLM and the second for common inferences about different probability maps. The methods introduced extend the idea of conjunction analysis to a full propositional logic and adapt it from test-statistics to Bayesian statistics. The new approaches allow inferences that are not possible with known standard methods in fMRI. (c) 2008 Wiley-Liss, Inc.

  9. A Bayesian Analysis of the Radioactive Releases of Fukushima

    DEFF Research Database (Denmark)

    Tomioka, Ryota; Mørup, Morten

    2012-01-01

    the types of nuclides and their levels of concentration from the recorded mixture of radiations to take necessary measures. We presently formulate a Bayesian generative model for the data available on radioactive releases from the Fukushima Daiichi disaster across Japan. From the sparsely sampled...... the Fukushima Daiichi plant we establish that the model is able to account for the data. We further demonstrate how the model extends to include all the available measurements recorded throughout Japan. The model can be considered a first attempt to apply Bayesian learning unsupervised in order to give a more......The Fukushima Daiichi disaster 11 March, 2011 is considered the largest nuclear accident since the 1986 Chernobyl disaster and has been rated at level 7 on the International Nuclear Event Scale. As different radioactive materials have different effects to human body, it is important to know...

  10. A Dynamic Bayesian Approach to Computational Laban Shape Quality Analysis

    Directory of Open Access Journals (Sweden)

    Dilip Swaminathan

    2009-01-01

    kinesiology. LMA (especially Effort/Shape emphasizes how internal feelings and intentions govern the patterning of movement throughout the whole body. As we argue, a complex understanding of intention via LMA is necessary for human-computer interaction to become embodied in ways that resemble interaction in the physical world. We thus introduce a novel, flexible Bayesian fusion approach for identifying LMA Shape qualities from raw motion capture data in real time. The method uses a dynamic Bayesian network (DBN to fuse movement features across the body and across time and as we discuss can be readily adapted for low-cost video. It has delivered excellent performance in preliminary studies comprising improvisatory movements. Our approach has been incorporated in Response, a mixed-reality environment where users interact via natural, full-body human movement and enhance their bodily-kinesthetic awareness through immersive sound and light feedback, with applications to kinesiology training, Parkinson's patient rehabilitation, interactive dance, and many other areas.

  11. Bayesian models for comparative analysis integrating phylogenetic uncertainty

    Science.gov (United States)

    2012-01-01

    Background Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. Methods We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. Results We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Conclusions Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for

  12. Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants

    KAUST Repository

    Jin, Ick Hoon

    2014-03-01

    Statistical inference for the models with intractable normalizing constants has attracted much attention. During the past two decades, various approximation- or simulation-based methods have been proposed for the problem, such as the Monte Carlo maximum likelihood method and the auxiliary variable Markov chain Monte Carlo methods. The Bayesian stochastic approximation Monte Carlo algorithm specifically addresses this problem: It works by sampling from a sequence of approximate distributions with their average converging to the target posterior distribution, where the approximate distributions can be achieved using the stochastic approximation Monte Carlo algorithm. A strong law of large numbers is established for the Bayesian stochastic approximation Monte Carlo estimator under mild conditions. Compared to the Monte Carlo maximum likelihood method, the Bayesian stochastic approximation Monte Carlo algorithm is more robust to the initial guess of model parameters. Compared to the auxiliary variable MCMC methods, the Bayesian stochastic approximation Monte Carlo algorithm avoids the requirement for perfect samples, and thus can be applied to many models for which perfect sampling is not available or very expensive. The Bayesian stochastic approximation Monte Carlo algorithm also provides a general framework for approximate Bayesian analysis. © 2012 Elsevier B.V. All rights reserved.

  13. Bayesian inference – a way to combine statistical data and semantic analysis meaningfully

    Directory of Open Access Journals (Sweden)

    Eila Lindfors

    2011-11-01

    Full Text Available This article focuses on presenting the possibilities of Bayesian modelling (Finite Mixture Modelling in the semantic analysis of statistically modelled data. The probability of a hypothesis in relation to the data available is an important question in inductive reasoning. Bayesian modelling allows the researcher to use many models at a time and provides tools to evaluate the goodness of different models. The researcher should always be aware that there is no such thing as the exact probability of an exact event. This is the reason for using probabilistic models. Each model presents a different perspective on the phenomenon in focus, and the researcher has to choose the most probable model with a view to previous research and the knowledge available.The idea of Bayesian modelling is illustrated here by presenting two different sets of data, one from craft science research (n=167 and the other (n=63 from educational research (Lindfors, 2007, 2002. The principles of how to build models and how to combine different profiles are described in the light of the research mentioned.Bayesian modelling is an analysis based on calculating probabilities in relation to a specific set of quantitative data. It is a tool for handling data and interpreting it semantically. The reliability of the analysis arises from an argumentation of which model can be selected from the model space as the basis for an interpretation, and on which arguments.Keywords: method, sloyd, Bayesian modelling, student teachersURN:NBN:no-29959

  14. Spatial Dependence and Heterogeneity in Bayesian Factor Analysis: A Cross-National Investigation of Schwartz Values

    Science.gov (United States)

    Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel

    2012-01-01

    In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…

  15. Nuclear stockpile stewardship and Bayesian image analysis (DARHT and the BIE)

    Energy Technology Data Exchange (ETDEWEB)

    Carroll, James L [Los Alamos National Laboratory

    2011-01-11

    Since the end of nuclear testing, the reliability of our nation's nuclear weapon stockpile has been performed using sub-critical hydrodynamic testing. These tests involve some pretty 'extreme' radiography. We will be discussing the challenges and solutions to these problems provided by DARHT (the world's premiere hydrodynamic testing facility) and the BIE or Bayesian Inference Engine (a powerful radiography analysis software tool). We will discuss the application of Bayesian image analysis techniques to this important and difficult problem.

  16. [Meta analysis of the use of Bayesian networks in breast cancer diagnosis].

    Science.gov (United States)

    Simões, Priscyla Waleska; Silva, Geraldo Doneda da; Moretti, Gustavo Pasquali; Simon, Carla Sasso; Winnikow, Erik Paul; Nassar, Silvia Modesto; Medeiros, Lidia Rosi; Rosa, Maria Inês

    2015-01-01

    The aim of this study was to determine the accuracy of Bayesian networks in supporting breast cancer diagnoses. Systematic review and meta-analysis were carried out, including articles and papers published between January 1990 and March 2013. We included prospective and retrospective cross-sectional studies of the accuracy of diagnoses of breast lesions (target conditions) made using Bayesian networks (index test). Four primary studies that included 1,223 breast lesions were analyzed, 89.52% (444/496) of the breast cancer cases and 6.33% (46/727) of the benign lesions were positive based on the Bayesian network analysis. The area under the curve (AUC) for the summary receiver operating characteristic curve (SROC) was 0.97, with a Q* value of 0.92. Using Bayesian networks to diagnose malignant lesions increased the pretest probability of a true positive from 40.03% to 90.05% and decreased the probability of a false negative to 6.44%. Therefore, our results demonstrated that Bayesian networks provide an accurate and non-invasive method to support breast cancer diagnosis.

  17. Analysis of Climate Change on Hydrologic Components by using Bayesian Neural Networks

    Science.gov (United States)

    Kang, K.

    2012-12-01

    Representation of hydrologic analysis in climate change is a challenging task. Hydrologic outputs in regional climate models (RCMs) from general circulation models (GCMs) have difficult representation due to several uncertainties in hydrologic impacts of climate change. To overcome this problem, this research presents practical options for hydrological climate change with Bayesian and Neural networks approached to regional adaption to climate change. Bayesian and Neural networks analysis to climate hydrologic components is one of new frontier researches considering to climate change expectation. Strong advantage in Bayesian Neural networks is detecting time series in hydrologic components, which is complicated due to data, parameter, and model hypothesis on climate change scenario, through changing steps by removing and adding connections in Neural network process that combined Bayesian concept from parameter, predict and update process. As an example study, Mekong River Watershed, which is surrounded by four countries (Myanmar, Laos, Thailand and Cambodia), is selected. Results will show understanding of hydrologic components trend on climate model simulations through Bayesian Neural networks.

  18. Bayesian Analysis of Demand Elasticity in the Italian Electricity Market

    Directory of Open Access Journals (Sweden)

    Maria Chiara D'Errico

    2015-09-01

    Full Text Available The liberalization of the Italian electricity market is a decade old. Within these last ten years, the supply side has been extensively analyzed, but not the demand side. The aim of this paper is to provide a new method for estimation of the demand elasticity, based on Bayesian methods applied to the Italian electricity market. We used individual demand bids data in the day-ahead market in the Italian Power Exchange (IPEX, for 2011, in order to construct an aggregate demand function at the hourly level. We took into account the existence of both elastic and inelastic bidders on the demand side. The empirical results show that elasticity varies significantly during the day and across periods of the year. In addition, the elasticity hourly distribution is clearly skewed and more so in the daily hours. The Bayesian method is a useful tool for policy-making, insofar as the regulator can start with a priori historical information on market behavior and estimate actual market outcomes in response to new policy actions.

  19. Bayesian analysis of deterministic and stochastic prisoner's dilemma games

    Directory of Open Access Journals (Sweden)

    Howard Kunreuther

    2009-08-01

    Full Text Available This paper compares the behavior of individuals playing a classic two-person deterministic prisoner's dilemma (PD game with choice data obtained from repeated interdependent security prisoner's dilemma games with varying probabilities of loss and the ability to learn (or not learn about the actions of one's counterpart, an area of recent interest in experimental economics. This novel data set, from a series of controlled laboratory experiments, is analyzed using Bayesian hierarchical methods, the first application of such methods in this research domain. We find that individuals are much more likely to be cooperative when payoffs are deterministic than when the outcomes are probabilistic. A key factor explaining this difference is that subjects in a stochastic PD game respond not just to what their counterparts did but also to whether or not they suffered a loss. These findings are interpreted in the context of behavioral theories of commitment, altruism and reciprocity. The work provides a linkage between Bayesian statistics, experimental economics, and consumer psychology.

  20. Expert prior elicitation and Bayesian analysis of the Mycotic Ulcer Treatment Trial I.

    Science.gov (United States)

    Sun, Catherine Q; Prajna, N Venkatesh; Krishnan, Tiruvengada; Mascarenhas, Jeena; Rajaraman, Revathi; Srinivasan, Muthiah; Raghavan, Anita; O'Brien, Kieran S; Ray, Kathryn J; McLeod, Stephen D; Porco, Travis C; Acharya, Nisha R; Lietman, Thomas M

    2013-06-14

    To perform a Bayesian analysis of the Mycotic Ulcer Treatment Trial I (MUTT I) using expert opinion as a prior belief. MUTT I was a randomized clinical trial comparing topical natamycin or voriconazole for treating filamentous fungal keratitis. A questionnaire elicited expert opinion on the best treatment of fungal keratitis before MUTT I results were available. A Bayesian analysis was performed using the questionnaire data as a prior belief and the MUTT I primary outcome (3-month visual acuity) by frequentist analysis as a likelihood. Corneal experts had a 41.1% prior belief that natamycin improved 3-month visual acuity compared with voriconazole. The Bayesian analysis found a 98.4% belief for natamycin treatment compared with voriconazole treatment for filamentous cases as a group (mean improvement 1.1 Snellen lines, 95% credible interval 0.1-2.1). The Bayesian analysis estimated a smaller treatment effect than the MUTT I frequentist analysis result of 1.8-line improvement with natamycin versus voriconazole (95% confidence interval 0.5-3.0, P = 0.006). For Fusarium cases, the posterior demonstrated a 99.7% belief for natamycin treatment, whereas non-Fusarium cases had a 57.3% belief. The Bayesian analysis suggests that natamycin is superior to voriconazole when filamentous cases are analyzed as a group. Subgroup analysis of Fusarium cases found improvement with natamycin compared with voriconazole, whereas there was almost no difference between treatments for non-Fusarium cases. These results were consistent with, though smaller in effect size than, the MUTT I primary outcome by frequentist analysis. The accordance between analyses further validates the trial results. (ClinicalTrials.gov number, NCT00996736.).

  1. Multilevel Bayesian networks for the analysis of hierarchical health care data.

    Science.gov (United States)

    Lappenschaar, Martijn; Hommersom, Arjen; Lucas, Peter J F; Lagro, Joep; Visscher, Stefan

    2013-03-01

    Large health care datasets normally have a hierarchical structure, in terms of levels, as the data have been obtained from different practices, hospitals, or regions. Multilevel regression is the technique commonly used to deal with such multilevel data. However, for the statistical analysis of interactions between entities from a domain, multilevel regression yields little to no insight. While Bayesian networks have proved to be useful for analysis of interactions, they do not have the capability to deal with hierarchical data. In this paper, we describe a new formalism, which we call multilevel Bayesian networks; its effectiveness for the analysis of hierarchically structured health care data is studied from the perspective of multimorbidity. Multilevel Bayesian networks are formally defined and applied to analyze clinical data from family practices in The Netherlands with the aim to predict interactions between heart failure and diabetes mellitus. We compare the results obtained with multilevel regression. The results obtained by multilevel Bayesian networks closely resembled those obtained by multilevel regression. For both diseases, the area under the curve of the prediction model improved, and the net reclassification improvements were significantly positive. In addition, the models offered considerable more insight, through its internal structure, into the interactions between the diseases. Multilevel Bayesian networks offer a suitable alternative to multilevel regression when analyzing hierarchical health care data. They provide more insight into the interactions between multiple diseases. Moreover, a multilevel Bayesian network model can be used for the prediction of the occurrence of multiple diseases, even when some of the predictors are unknown, which is typically the case in medicine. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. Estimating size and scope economies in the Portuguese water sector using the Bayesian stochastic frontier analysis

    Energy Technology Data Exchange (ETDEWEB)

    Carvalho, Pedro, E-mail: pedrocarv@coc.ufrj.br [Computational Modelling in Engineering and Geophysics Laboratory (LAMEMO), Department of Civil Engineering, COPPE, Federal University of Rio de Janeiro, Av. Pedro Calmon - Ilha do Fundão, 21941-596 Rio de Janeiro (Brazil); Center for Urban and Regional Systems (CESUR), CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Marques, Rui Cunha, E-mail: pedro.c.carvalho@tecnico.ulisboa.pt [Center for Urban and Regional Systems (CESUR), CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon (Portugal)

    2016-02-15

    This study aims to search for economies of size and scope in the Portuguese water sector applying Bayesian and classical statistics to make inference in stochastic frontier analysis (SFA). This study proves the usefulness and advantages of the application of Bayesian statistics for making inference in SFA over traditional SFA which just uses classical statistics. The resulting Bayesian methods allow overcoming some problems that arise in the application of the traditional SFA, such as the bias in small samples and skewness of residuals. In the present case study of the water sector in Portugal, these Bayesian methods provide more plausible and acceptable results. Based on the results obtained we found that there are important economies of output density, economies of size, economies of vertical integration and economies of scope in the Portuguese water sector, pointing out to the huge advantages in undertaking mergers by joining the retail and wholesale components and by joining the drinking water and wastewater services. - Highlights: • This study aims to search for economies of size and scope in the water sector; • The usefulness of the application of Bayesian methods is highlighted; • Important economies of output density, economies of size, economies of vertical integration and economies of scope are found.

  3. Estimating size and scope economies in the Portuguese water sector using the Bayesian stochastic frontier analysis

    International Nuclear Information System (INIS)

    Carvalho, Pedro; Marques, Rui Cunha

    2016-01-01

    This study aims to search for economies of size and scope in the Portuguese water sector applying Bayesian and classical statistics to make inference in stochastic frontier analysis (SFA). This study proves the usefulness and advantages of the application of Bayesian statistics for making inference in SFA over traditional SFA which just uses classical statistics. The resulting Bayesian methods allow overcoming some problems that arise in the application of the traditional SFA, such as the bias in small samples and skewness of residuals. In the present case study of the water sector in Portugal, these Bayesian methods provide more plausible and acceptable results. Based on the results obtained we found that there are important economies of output density, economies of size, economies of vertical integration and economies of scope in the Portuguese water sector, pointing out to the huge advantages in undertaking mergers by joining the retail and wholesale components and by joining the drinking water and wastewater services. - Highlights: • This study aims to search for economies of size and scope in the water sector; • The usefulness of the application of Bayesian methods is highlighted; • Important economies of output density, economies of size, economies of vertical integration and economies of scope are found.

  4. Introduction to Bayesian statistics

    CERN Document Server

    Bolstad, William M

    2017-01-01

    There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...

  5. Bayesian analysis of repairable systems showing a bounded failure intensity

    International Nuclear Information System (INIS)

    Guida, Maurizio; Pulcini, Gianpaolo

    2006-01-01

    The failure pattern of repairable mechanical equipment subject to deterioration phenomena sometimes shows a finite bound for the increasing failure intensity. A non-homogeneous Poisson process with bounded increasing failure intensity is then illustrated and its characteristics are discussed. A Bayesian procedure, based on prior information on model-free quantities, is developed in order to allow technical information on the failure process to be incorporated into the inferential procedure and to improve the inference accuracy. Posterior estimation of the model-free quantities and of other quantities of interest (such as the optimal replacement interval) is provided, as well as prediction on the waiting time to the next failure and on the number of failures in a future time interval is given. Finally, numerical examples are given to illustrate the proposed inferential procedure

  6. FABADA: a Fitting Algorithm for Bayesian Analysis of DAta

    International Nuclear Information System (INIS)

    Pardo, L C; Rovira-Esteva, M; Ruiz-Martin, M D; Tamarit, J Ll; Busch, S

    2011-01-01

    The fit of data using a mathematical model is the standard way to know if the model describes data correctly and to obtain parameters that describe the physical processes hidden behind the experimental results. This is usually done by means of a χ 2 minimization procedure. Although this procedure is fast and quite reliable for simple models, it has many drawbacks when dealing with complicated problems such as models with many or correlated parameters. We present here a Bayesian method to explore the parameter space guided only by the probability laws underlying the χ 2 figure of merit. The presented method does not get stuck in local minima of the χ 2 landscape as it usually happens with classical minimization procedures. Moreover correlations between parameters are taken into account in a natural way. Finally, parameters are obtained as probability distribution functions so that all the complexity of the parameter space is shown.

  7. Micronutrients in HIV: a Bayesian meta-analysis.

    Directory of Open Access Journals (Sweden)

    George M Carter

    Full Text Available Approximately 28.5 million people living with HIV are eligible for treatment (CD4<500, but currently have no access to antiretroviral therapy. Reduced serum level of micronutrients is common in HIV disease. Micronutrient supplementation (MNS may mitigate disease progression and mortality.We synthesized evidence on the effect of micronutrient supplementation on mortality and rate of disease progression in HIV disease.We searched MEDLINE, EMBASE, the Cochrane Central, AMED and CINAHL databases through December 2014, without language restriction, for studies of greater than 3 micronutrients versus any or no comparator. We built a hierarchical Bayesian random effects model to synthesize results. Inferences are based on the posterior distribution of the population effects; posterior distributions were approximated by Markov chain Monte Carlo in OpenBugs.From 2166 initial references, we selected 49 studies for full review and identified eight reporting on disease progression and/or mortality. Bayesian synthesis of data from 2,249 adults in three studies estimated the relative risk of disease progression in subjects on MNS vs. control as 0.62 (95% credible interval, 0.37, 0.96. Median number needed to treat is 8.4 (4.8, 29.9 and the Bayes Factor 53.4. Based on data reporting on 4,095 adults reporting mortality in 7 randomized controlled studies, the RR was 0.84 (0.38, 1.85, NNT is 25 (4.3, ∞.MNS significantly and substantially slows disease progression in HIV+ adults not on ARV, and possibly reduces mortality. Micronutrient supplements are effective in reducing progression with a posterior probability of 97.9%. Considering MNS low cost and lack of adverse effects, MNS should be standard of care for HIV+ adults not yet on ARV.

  8. Application of change-point problem to the detection of plant patches.

    Science.gov (United States)

    López, I; Gámez, M; Garay, J; Standovár, T; Varga, Z

    2010-03-01

    In ecology, if the considered area or space is large, the spatial distribution of individuals of a given plant species is never homogeneous; plants form different patches. The homogeneity change in space or in time (in particular, the related change-point problem) is an important research subject in mathematical statistics. In the paper, for a given data system along a straight line, two areas are considered, where the data of each area come from different discrete distributions, with unknown parameters. In the paper a method is presented for the estimation of the distribution change-point between both areas and an estimate is given for the distributions separated by the obtained change-point. The solution of this problem will be based on the maximum likelihood method. Furthermore, based on an adaptation of the well-known bootstrap resampling, a method for the estimation of the so-called change-interval is also given. The latter approach is very general, since it not only applies in the case of the maximum-likelihood estimation of the change-point, but it can be also used starting from any other change-point estimation known in the ecological literature. The proposed model is validated against typical ecological situations, providing at the same time a verification of the applied algorithms.

  9. Using Discrete Loss Functions and Weighted Kappa for Classification: An Illustration Based on Bayesian Network Analysis

    Science.gov (United States)

    Zwick, Rebecca; Lenaburg, Lubella

    2009-01-01

    In certain data analyses (e.g., multiple discriminant analysis and multinomial log-linear modeling), classification decisions are made based on the estimated posterior probabilities that individuals belong to each of several distinct categories. In the Bayesian network literature, this type of classification is often accomplished by assigning…

  10. Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

    Science.gov (United States)

    Herskovits, Edward H.; Gerring, Joan P.

    2003-01-01

    Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

  11. Reporting of Bayesian analysis in epidemiologic research should become more transparent

    NARCIS (Netherlands)

    Rietbergen, Charlotte; Debray, Thomas P. A.; Klugkist, Irene; Janssen, Kristel J M; Moons, Karel G. M.

    Objectives The objective of this systematic review is to investigate the use of Bayesian data analysis in epidemiology in the past decade and particularly to evaluate the quality of research papers reporting the results of these analyses. Study Design and Setting Complete volumes of five major

  12. Calibration of Uncertainty Analysis of the SWAT Model Using Genetic Algorithms and Bayesian Model Averaging

    Science.gov (United States)

    In this paper, the Genetic Algorithms (GA) and Bayesian model averaging (BMA) were combined to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this hybrid method, several SWAT models with different structures are first selected; next GA i...

  13. Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences.

    Science.gov (United States)

    Lu, Zhao-Hua; Chow, Sy-Miin; Loken, Eric

    2016-01-01

    Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.

  14. A Pragmatic Bayesian Perspective on Correlation Analysis : The exoplanetary gravity - stellar activity case.

    Science.gov (United States)

    Figueira, P; Faria, J P; Adibekyan, V Zh; Oshagh, M; Santos, N C

    2016-11-01

    We apply the Bayesian framework to assess the presence of a correlation between two quantities. To do so, we estimate the probability distribution of the parameter of interest, ρ, characterizing the strength of the correlation. We provide an implementation of these ideas and concepts using python programming language and the pyMC module in a very short (∼ 130 lines of code, heavily commented) and user-friendly program. We used this tool to assess the presence and properties of the correlation between planetary surface gravity and stellar activity level as measured by the log([Formula: see text]) indicator. The results of the Bayesian analysis are qualitatively similar to those obtained via p-value analysis, and support the presence of a correlation in the data. The results are more robust in their derivation and more informative, revealing interesting features such as asymmetric posterior distributions or markedly different credible intervals, and allowing for a deeper exploration. We encourage the reader interested in this kind of problem to apply our code to his/her own scientific problems. The full understanding of what the Bayesian framework is can only be gained through the insight that comes by handling priors, assessing the convergence of Monte Carlo runs, and a multitude of other practical problems. We hope to contribute so that Bayesian analysis becomes a tool in the toolkit of researchers, and they understand by experience its advantages and limitations.

  15. Bayesian Integrated Data Analysis of Fast-Ion Measurements by Velocity-Space Tomography

    DEFF Research Database (Denmark)

    Salewski, M.; Nocente, M.; Jacobsen, A.S.

    2018-01-01

    Bayesian integrated data analysis combines measurements from different diagnostics to jointly measure plasma parameters of interest such as temperatures, densities, and drift velocities. Integrated data analysis of fast-ion measurements has long been hampered by the complexity of the strongly non...... framework. The implementation for different types of diagnostics as well as the uncertainties are discussed, and we highlight the importance of integrated data analysis of all available detectors....

  16. Bayesian Analysis of Hot Jupiter Radii Points to Ohmic Dissipation

    Science.gov (United States)

    Thorngren, Daniel; Fortney, Jonathan J.

    2017-10-01

    The cause of the unexpectedly large radii of hot Jupiters has been the subject of many hypotheses over the past 15 years and is one of the long-standing open issues in exoplanetary physics. In our work, we seek to examine the population of 300 hot Jupiters to identify a model that best explains their radii. Using a hierarchical Bayesian framework, we match structure evolution models to the observed giant planets’ masses, radii, and ages, with a prior for bulk composition based on the mass from Thorngren et al. (2016). We consider various models for the relationship between heating efficiency (the fraction of flux absorbed into the interior) and incident flux. For the first time, we are able to derive this heating efficiency as a function of planetary T_eq. Models in which the heating efficiency decreases at the higher temperatures (above ~1600 K) are strongly and statistically significantly preferred. Of the published models for the radius anomaly, only the Ohmic dissipation model predicts this feature, which it explains as being the result of magnetic drag reducing atmospheric wind speeds. We interpret our results as strong evidence in favor of the Ohmic dissipation model.

  17. Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making

    Science.gov (United States)

    Bahrami, Bahador; Latham, Peter E.

    2015-01-01

    Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people’s confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality. PMID:26517475

  18. Sensitivity analysis in Gaussian Bayesian networks using a symbolic-numerical technique

    International Nuclear Information System (INIS)

    Castillo, Enrique; Kjaerulff, Uffe

    2003-01-01

    The paper discusses the problem of sensitivity analysis in Gaussian Bayesian networks. The algebraic structure of the conditional means and variances, as rational functions involving linear and quadratic functions of the parameters, are used to simplify the sensitivity analysis. In particular the probabilities of conditional variables exceeding given values and related probabilities are analyzed. Two examples of application are used to illustrate all the concepts and methods

  19. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

    Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  20. Bayesian model and spatial analysis of oral and oropharynx cancer mortality in Minas Gerais, Brazil.

    Science.gov (United States)

    Fonseca, Emílio Prado da; Oliveira, Cláudia Di Lorenzo; Chiaravalloti, Francisco; Pereira, Antonio Carlos; Vedovello, Silvia Amélia Scudeler; Meneghim, Marcelo de Castro

    2018-01-01

    The objective of this study was to determine of oral and oropharynx cancer mortality rate and the results were analyzed by applying the Spatial Analysis of Empirical Bayesian Model. To this end, we used the information contained in the International Classification of Diseases (ICD-10), Chapter II, Category C00 to C14 and Brazilian Mortality Information System (SIM) of Minas Gerais State. Descriptive statistics were observed and the gross rate of mortality was calculated for each municipality. Then Empirical Bayesian estimators were applied. The results showed that, in 2012, in the state of Minas Gerais, were registered 769 deaths of patients with cancer of oral and oropharynx, with 607 (78.96%) men and 162 (21.04%) women. There was a wide variation in spatial distribution of crude mortality rate and were identified agglomeration in the South, Central and North more accurately by Bayesian Estimator Global and Local Model. Through Bayesian models was possible to map the spatial clustering of deaths from oral cancer more accurately, and with the application of the method of spatial epidemiology, it was possible to obtain more accurate results and provide subsidies to reduce the number of deaths from this type of cancer.

  1. Estimating size and scope economies in the Portuguese water sector using the Bayesian stochastic frontier analysis.

    Science.gov (United States)

    Carvalho, Pedro; Marques, Rui Cunha

    2016-02-15

    This study aims to search for economies of size and scope in the Portuguese water sector applying Bayesian and classical statistics to make inference in stochastic frontier analysis (SFA). This study proves the usefulness and advantages of the application of Bayesian statistics for making inference in SFA over traditional SFA which just uses classical statistics. The resulting Bayesian methods allow overcoming some problems that arise in the application of the traditional SFA, such as the bias in small samples and skewness of residuals. In the present case study of the water sector in Portugal, these Bayesian methods provide more plausible and acceptable results. Based on the results obtained we found that there are important economies of output density, economies of size, economies of vertical integration and economies of scope in the Portuguese water sector, pointing out to the huge advantages in undertaking mergers by joining the retail and wholesale components and by joining the drinking water and wastewater services. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations.

    Science.gov (United States)

    Majumdar, Arunabha; Haldar, Tanushree; Bhattacharya, Sourabh; Witte, John S

    2018-02-01

    Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

  3. A Pareto scale-inflated outlier model and its Bayesian analysis

    OpenAIRE

    Scollnik, David P. M.

    2016-01-01

    This paper develops a Pareto scale-inflated outlier model. This model is intended for use when data from some standard Pareto distribution of interest is suspected to have been contaminated with a relatively small number of outliers from a Pareto distribution with the same shape parameter but with an inflated scale parameter. The Bayesian analysis of this Pareto scale-inflated outlier model is considered and its implementation using the Gibbs sampler is discussed. The paper contains three wor...

  4. Environmental Modeling and Bayesian Analysis for Assessing Human Health Impacts from Radioactive Waste Disposal

    Science.gov (United States)

    Stockton, T.; Black, P.; Tauxe, J.; Catlett, K.

    2004-12-01

    Bayesian decision analysis provides a unified framework for coherent decision-making. Two key components of Bayesian decision analysis are probability distributions and utility functions. Calculating posterior distributions and performing decision analysis can be computationally challenging, especially for complex environmental models. In addition, probability distributions and utility functions for environmental models must be specified through expert elicitation, stakeholder consensus, or data collection, all of which have their own set of technical and political challenges. Nevertheless, a grand appeal of the Bayesian approach for environmental decision- making is the explicit treatment of uncertainty, including expert judgment. The impact of expert judgment on the environmental decision process, though integral, goes largely unassessed. Regulations and orders of the Environmental Protection Agency, Department Of Energy, and Nuclear Regulatory Agency orders require assessing the impact on human health of radioactive waste contamination over periods of up to ten thousand years. Towards this end complex environmental simulation models are used to assess "risk" to human and ecological health from migration of radioactive waste. As the computational burden of environmental modeling is continually reduced probabilistic process modeling using Monte Carlo simulation is becoming routinely used to propagate uncertainty from model inputs through model predictions. The utility of a Bayesian approach to environmental decision-making is discussed within the context of a buried radioactive waste example. This example highlights the desirability and difficulties of merging the cost of monitoring, the cost of the decision analysis, the cost and viability of clean up, and the probability of human health impacts within a rigorous decision framework.

  5. Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration

    Directory of Open Access Journals (Sweden)

    Kelemen Arpad

    2008-08-01

    Full Text Available Abstract Background This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascades to therapeutic doses in multiple tissues such as liver, skeletal muscle, and kidney from the same animals. Affymetrix time course gene expression data U34A are obtained from three different tissues including kidney, liver and muscle. Our goal is not only to find the concordance of gene in different tissues, identify the common differentially expressed genes over time and also examine the reproducibility of the findings by integrating the results through meta analysis from multiple tissues in order to gain a significant increase in the power of detecting differentially expressed genes over time and to find the differential differences of three tissues responding to the drug. Results and conclusion Bayesian categorical model for estimating the proportion of the 'call' are used for pre-screening genes. Hierarchical Bayesian Mixture Model is further developed for the identifications of differentially expressed genes across time and dynamic clusters. Deviance information criterion is applied to determine the number of components for model comparisons and selections. Bayesian mixture model produces the gene-specific posterior probability of differential/non-differential expression and the 95% credible interval, which is the basis for our further Bayesian meta-inference. Meta-analysis is performed in order to identify commonly expressed genes from multiple tissues that may serve as ideal targets for novel treatment strategies and to integrate the results across separate studies. We have found the common expressed genes in the three tissues. However, the up/down/no regulations of these common genes are different at different time points. Moreover, the most differentially expressed genes were found in the liver, then in kidney, and then in muscle.

  6. Dating ancient Chinese celadon porcelain by neutron activation analysis and bayesian classification

    International Nuclear Information System (INIS)

    Xie Guoxi; Feng Songlin; Feng Xiangqian; Zhu Jihao; Yan Lingtong; Li Li

    2009-01-01

    Dating ancient Chinese porcelain is one of the most important and difficult problems in porcelain archaeological field. Eighteen elements in bodies of ancient celadon porcelains fired in Southern Song to Yuan period (AD 1127-1368) and Ming dynasty (AD 1368-1644), including La, Sm, U, Ce, etc., were determined by neutron activation analysis (NAA). After the outliers of experimental data were excluded and multivariate normal distribution was tested, and Bayesian classification was used for dating of 165 ancient celadon porcelain samples. The results show that 98.2% of total ancient celadon porcelain samples are classified correctly. It means that NAA and Bayesian classification are very useful for dating ancient porcelain. (authors)

  7. Introduction of Bayesian network in risk analysis of maritime accidents in Bangladesh

    Science.gov (United States)

    Rahman, Sohanur

    2017-12-01

    Due to the unique geographic location, complex navigation environment and intense vessel traffic, a considerable number of maritime accidents occurred in Bangladesh which caused serious loss of life, property and environmental contamination. Based on the historical data of maritime accidents from 1981 to 2015, which has been collected from Department of Shipping (DOS) and Bangladesh Inland Water Transport Authority (BIWTA), this paper conducted a risk analysis of maritime accidents by applying Bayesian network. In order to conduct this study, a Bayesian network model has been developed to find out the relation among parameters and the probability of them which affect accidents based on the accident investigation report of Bangladesh. Furthermore, number of accidents in different categories has also been investigated in this paper. Finally, some viable recommendations have been proposed in order to ensure greater safety of inland vessels in Bangladesh.

  8. Dizzy-Beats: a Bayesian evidence analysis tool for systems biology.

    Science.gov (United States)

    Aitken, Stuart; Kilpatrick, Alastair M; Akman, Ozgur E

    2015-06-01

    Model selection and parameter inference are complex problems of long-standing interest in systems biology. Selecting between competing models arises commonly as underlying biochemical mechanisms are often not fully known, hence alternative models must be considered. Parameter inference yields important information on the extent to which the data and the model constrain parameter values. We report Dizzy-Beats, a graphical Java Bayesian evidence analysis tool implementing nested sampling - an algorithm yielding an estimate of the log of the Bayesian evidence Z and the moments of model parameters, thus addressing two outstanding challenges in systems modelling. A likelihood function based on the L1-norm is adopted as it is generically applicable to replicated time series data. http://sourceforge.net/p/bayesevidence/home/Home/. © The Author 2015. Published by Oxford University Press.

  9. Bayesian estimation of dynamic matching function for U-V analysis in Japan

    Science.gov (United States)

    Kyo, Koki; Noda, Hideo; Kitagawa, Genshiro

    2012-05-01

    In this paper we propose a Bayesian method for analyzing unemployment dynamics. We derive a Beveridge curve for unemployment and vacancy (U-V) analysis from a Bayesian model based on a labor market matching function. In our framework, the efficiency of matching and the elasticities of new hiring with respect to unemployment and vacancy are regarded as time varying parameters. To construct a flexible model and obtain reasonable estimates in an underdetermined estimation problem, we treat the time varying parameters as random variables and introduce smoothness priors. The model is then described in a state space representation, enabling the parameter estimation to be carried out using Kalman filter and fixed interval smoothing. In such a representation, dynamic features of the cyclic unemployment rate and the structural-frictional unemployment rate can be accurately captured.

  10. Risks Analysis of Logistics Financial Business Based on Evidential Bayesian Network

    Directory of Open Access Journals (Sweden)

    Ying Yan

    2013-01-01

    Full Text Available Risks in logistics financial business are identified and classified. Making the failure of the business as the root node, a Bayesian network is constructed to measure the risk levels in the business. Three importance indexes are calculated to find the most important risks in the business. And more, considering the epistemic uncertainties in the risks, evidence theory associate with Bayesian network is used as an evidential network in the risk analysis of logistics finance. To find how much uncertainty in root node is produced by each risk, a new index, epistemic importance, is defined. Numerical examples show that the proposed methods could provide a lot of useful information. With the information, effective approaches could be found to control and avoid these sensitive risks, thus keep logistics financial business working more reliable. The proposed method also gives a quantitative measure of risk levels in logistics financial business, which provides guidance for the selection of financing solutions.

  11. Convergence analysis of surrogate-based methods for Bayesian inverse problems

    Science.gov (United States)

    Yan, Liang; Zhang, Yuan-Xiang

    2017-12-01

    The major challenges in the Bayesian inverse problems arise from the need for repeated evaluations of the forward model, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. Many attempts at accelerating Bayesian inference have relied on surrogates for the forward model, typically constructed through repeated forward simulations that are performed in an offline phase. Although such approaches can be quite effective at reducing computation cost, there has been little analysis of the approximation on posterior inference. In this work, we prove error bounds on the Kullback–Leibler (KL) distance between the true posterior distribution and the approximation based on surrogate models. Our rigorous error analysis show that if the forward model approximation converges at certain rate in the prior-weighted L 2 norm, then the posterior distribution generated by the approximation converges to the true posterior at least two times faster in the KL sense. The error bound on the Hellinger distance is also provided. To provide concrete examples focusing on the use of the surrogate model based methods, we present an efficient technique for constructing stochastic surrogate models to accelerate the Bayesian inference approach. The Christoffel least squares algorithms, based on generalized polynomial chaos, are used to construct a polynomial approximation of the forward solution over the support of the prior distribution. The numerical strategy and the predicted convergence rates are then demonstrated on the nonlinear inverse problems, involving the inference of parameters appearing in partial differential equations.

  12. bspmma: An R Package for Bayesian Semiparametric Models for Meta-Analysis

    Directory of Open Access Journals (Sweden)

    Deborah Burr

    2012-07-01

    Full Text Available We introduce an R package, bspmma, which implements a Dirichlet-based random effects model specific to meta-analysis. In meta-analysis, when combining effect estimates from several heterogeneous studies, it is common to use a random-effects model. The usual frequentist or Bayesian models specify a normal distribution for the true effects. However, in many situations, the effect distribution is not normal, e.g., it can have thick tails, be skewed, or be multi-modal. A Bayesian nonparametric model based on mixtures of Dirichlet process priors has been proposed in the literature, for the purpose of accommodating the non-normality. We review this model and then describe a competitor, a semiparametric version which has the feature that it allows for a well-defined centrality parameter convenient for determining whether the overall effect is significant. This second Bayesian model is based on a different version of the Dirichlet process prior, and we call it the "conditional Dirichlet model". The package contains functions to carry out analyses based on either the ordinary or the conditional Dirichlet model, functions for calculating certain Bayes factors that provide a check on the appropriateness of the conditional Dirichlet model, and functions that enable an empirical Bayes selection of the precision parameter of the Dirichlet process. We illustrate the use of the package on two examples, and give an interpretation of the results in these two different scenarios.

  13. Bayesian analysis of interacting quantitative trait loci (QTL) for yield ...

    African Journals Online (AJOL)

    7×Lycopersicon pimpinellifolium LA2184 was used for genome-wide linkage analysis for yield traits in tomato. The genetic map, spanning the tomato genome of 808.4 cM long was constructed with 112 SSR markers distributing on 16 linkage ...

  14. Flexible and efficient implementations of Bayesian independent component analysis

    DEFF Research Database (Denmark)

    Winther, Ole; Petersen, Kaare Brandt

    2007-01-01

    In this paper we present an empirical Bayes method for flexible and efficient independent component analysis (ICA). The method is flexible with respect to choice of source prior, dimensionality and constraints of the mixing matrix (unconstrained or non-negativity), and structure of the noise cova...... Elsevier B.V. All rights reserved....

  15. Bayesian statistics applied to neutron activation data for reactor flux spectrum analysis

    International Nuclear Information System (INIS)

    Chiesa, Davide; Previtali, Ezio; Sisti, Monica

    2014-01-01

    Highlights: • Bayesian statistics to analyze the neutron flux spectrum from activation data. • Rigorous statistical approach for accurate evaluation of the neutron flux groups. • Cross section and activation data uncertainties included for the problem solution. • Flexible methodology applied to analyze different nuclear reactor flux spectra. • The results are in good agreement with the MCNP simulations of neutron fluxes. - Abstract: In this paper, we present a statistical method, based on Bayesian statistics, to analyze the neutron flux spectrum from the activation data of different isotopes. The experimental data were acquired during a neutron activation experiment performed at the TRIGA Mark II reactor of Pavia University (Italy) in four irradiation positions characterized by different neutron spectra. In order to evaluate the neutron flux spectrum, subdivided in energy groups, a system of linear equations, containing the group effective cross sections and the activation rate data, has to be solved. However, since the system’s coefficients are experimental data affected by uncertainties, a rigorous statistical approach is fundamental for an accurate evaluation of the neutron flux groups. For this purpose, we applied the Bayesian statistical analysis, that allows to include the uncertainties of the coefficients and the a priori information about the neutron flux. A program for the analysis of Bayesian hierarchical models, based on Markov Chain Monte Carlo (MCMC) simulations, was used to define the problem statistical model and solve it. The first analysis involved the determination of the thermal, resonance-intermediate and fast flux components and the dependence of the results on the Prior distribution choice was investigated to confirm the reliability of the Bayesian analysis. After that, the main resonances of the activation cross sections were analyzed to implement multi-group models with finer energy subdivisions that would allow to determine the

  16. Modified Bayesian Kriging for Noisy Response Problems for Reliability Analysis

    Science.gov (United States)

    2015-01-01

    52242, USA nicholas-gaul@uiowa.edu Mary Kathryn Cowles Department of Statistics & Actuarial Science College of Liberal Arts and Sciences , The...Forrester, A. I. J., & Keane, A. J. (2009). Recent advances in surrogate-based optimization. Progress in Aerospace Sciences , 45(1–3), 50-79. doi...Wiley. [27] Sacks, J., Welch, W. J., Toby J. Mitchell, & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science , 4

  17. Bayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs.

    Science.gov (United States)

    Verardo, L L; Silva, F F; Varona, L; Resende, M D V; Bastiaansen, J W M; Lopes, P S; Guimarães, S E F

    2015-02-01

    The genetic improvement of reproductive traits such as the number of teats is essential to the success of the pig industry. As opposite to most SNP association studies that consider continuous phenotypes under Gaussian assumptions, this trait is characterized as a discrete variable, which could potentially follow other distributions, such as the Poisson. Therefore, in order to access the complexity of a counting random regression considering all SNPs simultaneously as covariate under a GWAS modeling, the Bayesian inference tools become necessary. Currently, another point that deserves to be highlighted in GWAS is the genetic dissection of complex phenotypes through candidate genes network derived from significant SNPs. We present a full Bayesian treatment of SNP association analysis for number of teats assuming alternatively Gaussian and Poisson distributions for this trait. Under this framework, significant SNP effects were identified by hypothesis tests using 95% highest posterior density intervals. These SNPs were used to construct associated candidate genes network aiming to explain the genetic mechanism behind this reproductive trait. The Bayesian model comparisons based on deviance posterior distribution indicated the superiority of Gaussian model. In general, our results suggest the presence of 19 significant SNPs, which mapped 13 genes. Besides, we predicted gene interactions through networks that are consistent with the mammals known breast biology (e.g., development of prolactin receptor signaling, and cell proliferation), captured known regulation binding sites, and provided candidate genes for that trait (e.g., TINAGL1 and ICK).

  18. Unavailability analysis of a PWR safety system by a Bayesian network

    International Nuclear Information System (INIS)

    Estevao, Lilian B.; Melo, Paulo Fernando F. Frutuoso e; Rivero, Jose J.

    2013-01-01

    Bayesian networks (BN) are directed acyclic graphs that have dependencies between variables, which are represented by nodes. These dependencies are represented by lines connecting the nodes and can be directed or not. Thus, it is possible to model conditional probabilities and calculate them with the help of Bayes' Theorem. The objective of this paper is to present the modeling of the failure of a safety system of a typical second generation light water reactor plant, the Containment Heat Removal System (CHRS), whose function is to cool the water of containment reservoir being recirculated through the Containment Spray Recirculation System (CSRS). CSRS is automatically initiated after a loss of coolant accident (LOCA) and together with the CHRS cools the reservoir water. The choice of this system was due to the fact that its analysis by a fault tree is available in Appendix II of the Reactor Safety Study Report (WASH-1400), and therefore all the necessary technical information is also available, such as system diagrams, failure data input and the fault tree itself that was developed to study system failure. The reason for the use of a bayesian network in this context was to assess its ability to reproduce the results of fault tree analyses and also verify the feasibility of treating dependent events. Comparing the fault trees and bayesian networks, the results obtained for the system failure were very close. (author)

  19. Inference on the Univariate Frailty Model: A Bayesian Reference Analysis Approach

    Science.gov (United States)

    Tomazella, Vera Lucia D.; Martins, Camila Bertini; Bernardo, Jose Miguel

    2008-11-01

    In this work we present an approach involving objective Bayesian reference analysis to the Frailty model with univariate survival time and sources of heterogeneity that are not captured by covariates. The derivation unconditional hazard and survival leads to the Lomax distribution, also known as the Pareto distribution of the second kind. This distribution has an important position in life testing to adjust data from business failures. Reference analysis, introduced by Bernardo (1979) produce a new solution for this problem. The results are illustrated with survival data analyzed in the literature and simulated data.

  20. Bayesian latent variable models for the analysis of experimental psychology data.

    Science.gov (United States)

    Merkle, Edgar C; Wang, Ting

    2018-02-01

    In this paper, we address the use of Bayesian factor analysis and structural equation models to draw inferences from experimental psychology data. While such application is non-standard, the models are generally useful for the unified analysis of multivariate data that stem from, e.g., subjects' responses to multiple experimental stimuli. We first review the models and the parameter identification issues inherent in the models. We then provide details on model estimation via JAGS and on Bayes factor estimation. Finally, we use the models to re-analyze experimental data on risky choice, comparing the approach to simpler, alternative methods.

  1. Bayesian meta-analysis models for microarray data: a comparative study

    Directory of Open Access Journals (Sweden)

    Song Joon J

    2007-03-01

    Full Text Available Abstract Background With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods. Results Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets

  2. Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

    Science.gov (United States)

    Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

    2011-01-01

    Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

  3. A Bayesian analysis of inflationary primordial spectrum models using Planck data

    Science.gov (United States)

    Santos da Costa, Simony; Benetti, Micol; Alcaniz, Jailson

    2018-03-01

    The current available Cosmic Microwave Background (CMB) data show an anomalously low value of the CMB temperature fluctuations at large angular scales (l physics. In this paper, we analyse a set of cutoff inflationary PPS models using a Bayesian model comparison approach in light of the latest CMB data from the Planck Collaboration. Our results show that the standard power-law parameterisation is preferred over all models considered in the analysis, which motivates the search for alternative explanations for the observed lack of power in the CMB anisotropy spectrum.

  4. Semiparametric Bayesian analysis of accelerated failure time models with cluster structures.

    Science.gov (United States)

    Li, Zhaonan; Xu, Xinyi; Shen, Junshan

    2017-11-10

    In this paper, we develop a Bayesian semiparametric accelerated failure time model for survival data with cluster structures. Our model allows distributional heterogeneity across clusters and accommodates their relationships through a density ratio approach. Moreover, a nonparametric mixture of Dirichlet processes prior is placed on the baseline distribution to yield full distributional flexibility. We illustrate through simulations that our model can greatly improve estimation accuracy by effectively pooling information from multiple clusters, while taking into account the heterogeneity in their random error distributions. We also demonstrate the implementation of our method using analysis of Mayo Clinic Trial in Primary Biliary Cirrhosis. Copyright © 2017 John Wiley & Sons, Ltd.

  5. Bayesian Analysis of Linear and Nonlinear Latent Variable Models with Fixed Covariate and Ordered Categorical Data

    Directory of Open Access Journals (Sweden)

    Thanoon Y. Thanoon

    2016-03-01

    Full Text Available In this paper, ordered categorical variables are used to compare between linear and nonlinear interactions of fixed covariate and latent variables Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Hidden continuous normal distribution (censored normal distribution is used to handle the problem of ordered categorical data. Statistical inferences, which involve estimation of parameters and their standard deviations, and residuals analyses for testing the selected model, are discussed. The proposed procedure is illustrated by a simulation data obtained from R program. Analysis are done by using OpenBUGS program.

  6. BayesLCA: An R Package for Bayesian Latent Class Analysis

    Directory of Open Access Journals (Sweden)

    Arthur White

    2014-11-01

    Full Text Available The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian setting. Three methods for fitting the model are provided, incorporating an expectation-maximization algorithm, Gibbs sampling and a variational Bayes approximation. The article briefly outlines the methodology behind each of these techniques and discusses some of the technical difficulties associated with them. Methods to remedy these problems are also described. Visualization methods for each of these techniques are included, as well as criteria to aid model selection.

  7. A Bayesian analysis of the chromosome architecture of human disorders by integrating reductionist data.

    Science.gov (United States)

    Emmert-Streib, Frank; de Matos Simoes, Ricardo; Tripathi, Shailesh; Glazko, Galina V; Dehmer, Matthias

    2012-01-01

    In this paper, we present a Bayesian approach to estimate a chromosome and a disorder network from the Online Mendelian Inheritance in Man (OMIM) database. In contrast to other approaches, we obtain statistic rather than deterministic networks enabling a parametric control in the uncertainty of the underlying disorder-disease gene associations contained in the OMIM, on which the networks are based. From a structural investigation of the chromosome network, we identify three chromosome subgroups that reflect architectural differences in chromosome-disorder associations that are predictively exploitable for a functional analysis of diseases.

  8. A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study

    Science.gov (United States)

    Kaplan, David; Chen, Jianshen

    2012-01-01

    A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for…

  9. CONSTRAINTS ON COSMIC-RAY PROPAGATION MODELS FROM A GLOBAL BAYESIAN ANALYSIS

    International Nuclear Information System (INIS)

    Trotta, R.; Johannesson, G.; Moskalenko, I. V.; Porter, T. A.; Ruiz de Austri, R.; Strong, A. W.

    2011-01-01

    Research in many areas of modern physics such as, e.g., indirect searches for dark matter and particle acceleration in supernova remnant shocks rely heavily on studies of cosmic rays (CRs) and associated diffuse emissions (radio, microwave, X-rays, γ-rays). While very detailed numerical models of CR propagation exist, a quantitative statistical analysis of such models has been so far hampered by the large computational effort that those models require. Although statistical analyses have been carried out before using semi-analytical models (where the computation is much faster), the evaluation of the results obtained from such models is difficult, as they necessarily suffer from many simplifying assumptions. The main objective of this paper is to present a working method for a full Bayesian parameter estimation for a numerical CR propagation model. For this study, we use the GALPROP code, the most advanced of its kind, which uses astrophysical information, and nuclear and particle data as inputs to self-consistently predict CRs, γ-rays, synchrotron, and other observables. We demonstrate that a full Bayesian analysis is possible using nested sampling and Markov Chain Monte Carlo methods (implemented in the SuperBayeS code) despite the heavy computational demands of a numerical propagation code. The best-fit values of parameters found in this analysis are in agreement with previous, significantly simpler, studies also based on GALPROP.

  10. Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification.

    Directory of Open Access Journals (Sweden)

    Fang Yan

    Full Text Available Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie analysis was proposed by mapping bow-tie analysis into Bayesian network (BN. Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures.

  11. Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification.

    Science.gov (United States)

    Yan, Fang; Xu, Kaili; Yao, Xiwen; Li, Yang

    2016-01-01

    Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA) is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie) analysis was proposed by mapping bow-tie analysis into Bayesian network (BN). Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures.

  12. Combination of Bayesian and Latent Semantic Analysis with Domain Specific Knowledge

    Directory of Open Access Journals (Sweden)

    Shen Lu

    2016-06-01

    Full Text Available With the development of information technology, electronic publications become popular. However, it is a challenge to retrieve information from electronic publications because the large amount of words, the synonymy problem and the polysemi problem. In this paper, we introduced a new algorithm called Bayesian Latent Semantic Analysis (BLSA. We chose to model text not based on terms but associations between words. Also, the significance of interesting features were improved by expand the number of similar terms with glossaries. Latent Semantic Analysis (LSA was chosen to discover significant features. Bayesian post probability was used to discover segmentation boundaries. Also, Dirchlet distribution was chosen to present the vector of topic distribution and calculate the maximum probability of the topics. Experimental results showed us that both Pk [8] and WindowsDiff [27] decreased 10% by using BLSA in comparison to the Lexical Cohesion with the original data. [8] Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K. and Harshman, R. (1990, 'Indexing by latent semantic analysis', Journal of the American Society for Information Science, vol. 41, n.6, pp. 391-407. [27] Pevzner, L. and Hearst, M.A. (2002. A critique and improvement of an evaluation metric for text segmentation, Computational Linguistics, vol. 28, no. 1, pp. 19-36.

  13. Meta-analysis of the effect of natural frequencies on Bayesian reasoning.

    Science.gov (United States)

    McDowell, Michelle; Jacobs, Perke

    2017-12-01

    The natural frequency facilitation effect describes the finding that people are better able to solve descriptive Bayesian inference tasks when represented as joint frequencies obtained through natural sampling, known as natural frequencies, than as conditional probabilities. The present meta-analysis reviews 20 years of research seeking to address when, why, and for whom natural frequency formats are most effective. We review contributions from research associated with the 2 dominant theoretical perspectives, the ecological rationality framework and nested-sets theory, and test potential moderators of the effect. A systematic review of relevant literature yielded 35 articles representing 226 performance estimates. These estimates were statistically integrated using a bivariate mixed-effects model that yields summary estimates of average performances across the 2 formats and estimates of the effects of different study characteristics on performance. These study characteristics range from moderators representing individual characteristics (e.g., numeracy, expertise), to methodological differences (e.g., use of incentives, scoring criteria) and features of problem representation (e.g., short menu format, visual aid). Short menu formats (less computationally complex representations showing joint-events) and visual aids demonstrated some of the strongest moderation effects, improving performance for both conditional probability and natural frequency formats. A number of methodological factors (e.g., exposure to both problem formats) were also found to affect performance rates, emphasizing the importance of a systematic approach. We suggest how research on Bayesian reasoning can be strengthened by broadening the definition of successful Bayesian reasoning to incorporate choice and process and by applying different research methodologies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  14. Effective connectivity analysis of default mode network based on the Bayesian network learning approach

    Science.gov (United States)

    Li, Rui; Chen, Kewei; Zhang, Nan; Fleisher, Adam S.; Li, Yao; Wu, Xia

    2009-02-01

    This work proposed to use the linear Gaussian Bayesian network (BN) to construct the effective connectivity model of the brain's default mode network (DMN), a set of regions characterized by more increased neural activity during rest-state than most goal-oriented tasks. In a complete unsupervised data-driven manner, Bayesian information criterion (BIC) based learning approach was utilized to identify a highest scored network whose nodes (brain regions) were selected based on the result from the group independent component analysis (Group ICA) examining the DMN. We put forward to adopt the statistical significance testing method for regression coefficients used in stepwise regression analysis to further refine the network identified by BIC. The final established BN, learned from the functional magnetic resonance imaging (fMRI) data acquired from 12 healthy young subjects during rest-state, revealed that the hippocampus (HC) was the most influential brain region that affected activities in all other regions included in the BN. In contrast, the posterior cingulate cortex (PCC) was influenced by other regions, but had no reciprocal effects on any other region. Overall, the configuration of our BN illustrated that a prominent connection from HC to PCC existed in the DMN.

  15. Evolutionary Analysis of Dengue Serotype 2 Viruses Using Phylogenetic and Bayesian Methods from New Delhi, India.

    Directory of Open Access Journals (Sweden)

    Nazia Afreen

    2016-03-01

    Full Text Available Dengue fever is the most important arboviral disease in the tropical and sub-tropical countries of the world. Delhi, the metropolitan capital state of India, has reported many dengue outbreaks, with the last outbreak occurring in 2013. We have recently reported predominance of dengue virus serotype 2 during 2011-2014 in Delhi. In the present study, we report molecular characterization and evolutionary analysis of dengue serotype 2 viruses which were detected in 2011-2014 in Delhi. Envelope genes of 42 DENV-2 strains were sequenced in the study. All DENV-2 strains grouped within the Cosmopolitan genotype and further clustered into three lineages; Lineage I, II and III. Lineage III replaced lineage I during dengue fever outbreak of 2013. Further, a novel mutation Thr404Ile was detected in the stem region of the envelope protein of a single DENV-2 strain in 2014. Nucleotide substitution rate and time to the most recent common ancestor were determined by molecular clock analysis using Bayesian methods. A change in effective population size of Indian DENV-2 viruses was investigated through Bayesian skyline plot. The study will be a vital road map for investigation of epidemiology and evolutionary pattern of dengue viruses in India.

  16. Evolutionary Analysis of Dengue Serotype 2 Viruses Using Phylogenetic and Bayesian Methods from New Delhi, India.

    Science.gov (United States)

    Afreen, Nazia; Naqvi, Irshad H; Broor, Shobha; Ahmed, Anwar; Kazim, Syed Naqui; Dohare, Ravins; Kumar, Manoj; Parveen, Shama

    2016-03-01

    Dengue fever is the most important arboviral disease in the tropical and sub-tropical countries of the world. Delhi, the metropolitan capital state of India, has reported many dengue outbreaks, with the last outbreak occurring in 2013. We have recently reported predominance of dengue virus serotype 2 during 2011-2014 in Delhi. In the present study, we report molecular characterization and evolutionary analysis of dengue serotype 2 viruses which were detected in 2011-2014 in Delhi. Envelope genes of 42 DENV-2 strains were sequenced in the study. All DENV-2 strains grouped within the Cosmopolitan genotype and further clustered into three lineages; Lineage I, II and III. Lineage III replaced lineage I during dengue fever outbreak of 2013. Further, a novel mutation Thr404Ile was detected in the stem region of the envelope protein of a single DENV-2 strain in 2014. Nucleotide substitution rate and time to the most recent common ancestor were determined by molecular clock analysis using Bayesian methods. A change in effective population size of Indian DENV-2 viruses was investigated through Bayesian skyline plot. The study will be a vital road map for investigation of epidemiology and evolutionary pattern of dengue viruses in India.

  17. Risk analysis of emergent water pollution accidents based on a Bayesian Network.

    Science.gov (United States)

    Tang, Caihong; Yi, Yujun; Yang, Zhifeng; Sun, Jie

    2016-01-01

    To guarantee the security of water quality in water transfer channels, especially in open channels, analysis of potential emergent pollution sources in the water transfer process is critical. It is also indispensable for forewarnings and protection from emergent pollution accidents. Bridges above open channels with large amounts of truck traffic are the main locations where emergent accidents could occur. A Bayesian Network model, which consists of six root nodes and three middle layer nodes, was developed in this paper, and was employed to identify the possibility of potential pollution risk. Dianbei Bridge is reviewed as a typical bridge on an open channel of the Middle Route of the South to North Water Transfer Project where emergent traffic accidents could occur. Risk of water pollutions caused by leakage of pollutants into water is focused in this study. The risk for potential traffic accidents at the Dianbei Bridge implies a risk for water pollution in the canal. Based on survey data, statistical analysis, and domain specialist knowledge, a Bayesian Network model was established. The human factor of emergent accidents has been considered in this model. Additionally, this model has been employed to describe the probability of accidents and the risk level. The sensitive reasons for pollution accidents have been deduced. The case has also been simulated that sensitive factors are in a state of most likely to lead to accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. A Bayesian framework for cell-level protein network analysis for multivariate proteomics image data

    Science.gov (United States)

    Kovacheva, Violet N.; Sirinukunwattana, Korsuk; Rajpoot, Nasir M.

    2014-03-01

    The recent development of multivariate imaging techniques, such as the Toponome Imaging System (TIS), has facilitated the analysis of multiple co-localisation of proteins. This could hold the key to understanding complex phenomena such as protein-protein interaction in cancer. In this paper, we propose a Bayesian framework for cell level network analysis allowing the identification of several protein pairs having significantly higher co-expression levels in cancerous tissue samples when compared to normal colon tissue. It involves segmenting the DAPI-labeled image into cells and determining the cell phenotypes according to their protein-protein dependence profile. The cells are phenotyped using Gaussian Bayesian hierarchical clustering (GBHC) after feature selection is performed. The phenotypes are then analysed using Difference in Sums of Weighted cO-dependence Profiles (DiSWOP), which detects differences in the co-expression patterns of protein pairs. We demonstrate that the pairs highlighted by the proposed framework have high concordance with recent results using a different phenotyping method. This demonstrates that the results are independent of the clustering method used. In addition, the highlighted protein pairs are further analysed via protein interaction pathway databases and by considering the localization of high protein-protein dependence within individual samples. This suggests that the proposed approach could identify potentially functional protein complexes active in cancer progression and cell differentiation.

  19. A discrete-time Bayesian network reliability modeling and analysis framework

    International Nuclear Information System (INIS)

    Boudali, H.; Dugan, J.B.

    2005-01-01

    Dependability tools are becoming an indispensable tool for modeling and analyzing (critical) systems. However the growing complexity of such systems calls for increasing sophistication of these tools. Dependability tools need to not only capture the complex dynamic behavior of the system components, but they must be also easy to use, intuitive, and computationally efficient. In general, current tools have a number of shortcomings including lack of modeling power, incapacity to efficiently handle general component failure distributions, and ineffectiveness in solving large models that exhibit complex dependencies between their components. We propose a novel reliability modeling and analysis framework based on the Bayesian network (BN) formalism. The overall approach is to investigate timed Bayesian networks and to find a suitable reliability framework for dynamic systems. We have applied our methodology to two example systems and preliminary results are promising. We have defined a discrete-time BN reliability formalism and demonstrated its capabilities from a modeling and analysis point of view. This research shows that a BN based reliability formalism is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, being based on the BN formalism, the framework is easy to use and intuitive for non-experts, and provides a basis for more advanced and useful analyses such as system diagnosis

  20. Exact Bayesian bin classification: a fast alternative to Bayesian classification and its application to neural response analysis.

    Science.gov (United States)

    Endres, D; Földiák, P

    2008-02-01

    We investigate the general problem of signal classification and, in particular, that of assigning stimulus labels to neural spike trains recorded from single cortical neurons. Finding efficient ways of classifying neural responses is especially important in experiments involving rapid presentation of stimuli. We introduce a fast, exact alternative to Bayesian classification. Instead of estimating the class-conditional densities p(x|y) (where x is a scalar function of the feature[s], y the class label) and converting them to P(y|x) via Bayes' theorem, this probability is evaluated directly and without the need for approximations. This is achieved by integrating over all possible binnings of x with an upper limit on the number of bins. Computational time is quadratic in both the number of observed data points and the number of bins. The algorithm also allows for the computation of feedback signals, which can be used as input to subsequent stages of inference, e.g. neural network training. Responses of single neurons from high-level visual cortex (area STSa) to rapid sequences of complex visual stimuli are analysed. Information latency and response duration increase nonlinearly with presentation duration, suggesting that neural processing speeds adapt to presentation speeds.

  1. Performance of an iterative two-stage bayesian technique for population pharmacokinetic analysis of rich data sets

    NARCIS (Netherlands)

    Proost, Johannes H.; Eleveld, Douglas J.

    2006-01-01

    Purpose. To test the suitability of an Iterative Two-Stage Bayesian (ITSB) technique for population pharmacokinetic analysis of rich data sets, and to compare ITSB with Standard Two-Stage (STS) analysis and nonlinear Mixed Effect Modeling (MEM). Materials and Methods. Data from a clinical study with

  2. Developing a Validation Methodology for Expert-Informed Bayesian Network Models Supporting Nuclear Nonproliferation Analysis

    Energy Technology Data Exchange (ETDEWEB)

    White, Amanda M.; Gastelum, Zoe N.; Whitney, Paul D.

    2014-05-13

    Under the auspices of Pacific Northwest National Laboratory’s Signature Discovery Initiative (SDI), the research team developed a series of Bayesian Network models to assess multi-source signatures of nuclear programs. A Bayesian network is a mathematical model that can be used to marshal evidence to assess competing hypotheses. The purpose of the models was to allow non-expert analysts to benefit from the use of expert-informed mathematical models to assess nuclear programs, because such assessments require significant technical expertise ranging from the nuclear fuel cycle, construction and engineering, imagery analysis, and so forth. One such model developed under this research was aimed at assessing the consistency of open-source information about a nuclear facility with the facility’s declared use. The model incorporates factors such as location, security and safety features among others identified by subject matter experts as crucial to their assessments. The model includes key features, observables and their relationships. The model also provides documentation, which serves as training materials for the non-experts.

  3. Assessing State Nuclear Weapons Proliferation: Using Bayesian Network Analysis of Social Factors

    Energy Technology Data Exchange (ETDEWEB)

    Coles, Garill A.; Brothers, Alan J.; Olson, Jarrod; Whitney, Paul D.

    2010-04-16

    A Bayesian network (BN) model of social factors can support proliferation assessments by estimating the likelihood that a state will pursue a nuclear weapon. Social factors including political, economic, nuclear capability, security, and national identity and psychology factors may play as important a role in whether a State pursues nuclear weapons as more physical factors. This paper will show how using Bayesian reasoning on a generic case of a would-be proliferator State can be used to combine evidence that supports proliferation assessment. Theories and analysis by political scientists can be leveraged in a quantitative and transparent way to indicate proliferation risk. BN models facilitate diagnosis and inference in a probabilistic environment by using a network of nodes and acyclic directed arcs between the nodes whose connections, or absence of, indicate probabilistic relevance, or independence. We propose a BN model that would use information from both traditional safeguards and the strengthened safeguards associated with the Additional Protocol to indicate countries with a high risk of proliferating nuclear weapons. This model could be used in a variety of applications such a prioritization tool and as a component of state safeguards evaluations. This paper will discuss the benefits of BN reasoning, the development of Pacific Northwest National Laboratory’s (PNNL) BN state proliferation model and how it could be employed as an analytical tool.

  4. Bayesian analysis of gene essentiality based on sequencing of transposon insertion libraries

    Science.gov (United States)

    DeJesus, Michael A.; Zhang, Yanjia J.; Sassetti, Christopher M.; Rubin, Eric J.; Sacchettini, James C.; Ioerger, Thomas R.

    2013-01-01

    Motivation: Next-generation sequencing affords an efficient analysis of transposon insertion libraries, which can be used to identify essential genes in bacteria. To analyse this high-resolution data, we present a formal Bayesian framework for estimating the posterior probability of essentiality for each gene, using the extreme-value distribution to characterize the statistical significance of the longest region lacking insertions within a gene. We describe a sampling procedure based on the Metropolis–Hastings algorithm to calculate posterior probabilities of essentiality while simultaneously integrating over unknown internal parameters. Results: Using a sequence dataset from a transposon library for Mycobacterium tuberculosis, we show that this Bayesian approach predicts essential genes that correspond well with genes shown to be essential in previous studies. Furthermore, we show that by using the extreme-value distribution to characterize genomic regions lacking transposon insertions, this method is capable of identifying essential domains within genes. This approach can be used for analysing transposon libraries in other organisms and augmenting essentiality predictions with statistical confidence scores. Availability: A python script implementing the method described is available for download from http://saclab.tamu.edu/essentiality/. Contact: michael.dejesus@tamu.edu or ioerger@cs.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23361328

  5. Bayesian soft x-ray tomography and MHD mode analysis on HL-2A

    Science.gov (United States)

    Li, Dong; Liu, Yi; Svensson, J.; Liu, Y. Q.; Song, X. M.; Yu, L. M.; Mao, Rui; Fu, B. Z.; Deng, Wei; Yuan, B. S.; Ji, X. Q.; Xu, Yuan; Chen, Wei; Zhou, Yan; Yang, Q. W.; Duan, X. R.; Liu, Yong; HL-2A Team

    2016-03-01

    A Bayesian based tomography method using so-called Gaussian processes (GPs) for the emission model has been applied to the soft x-ray (SXR) diagnostics on HL-2A tokamak. To improve the accuracy of reconstructions, the standard GP is extended to a non-stationary version so that different smoothness between the plasma center and the edge can be taken into account in the algorithm. The uncertainty in the reconstruction arising from measurement errors and incapability can be fully analyzed by the usage of Bayesian probability theory. In this work, the SXR reconstructions by this non-stationary Gaussian processes tomography (NSGPT) method have been compared with the equilibrium magnetic flux surfaces, generally achieving a satisfactory agreement in terms of both shape and position. In addition, singular-value-decomposition (SVD) and Fast Fourier Transform (FFT) techniques have been applied for the analysis of SXR and magnetic diagnostics, in order to explore the spatial and temporal features of the saturated long-lived magnetohydrodynamics (MHD) instability induced by energetic particles during neutral beam injection (NBI) on HL-2A. The result shows that this ideal internal kink instability has a dominant m/n  =  1/1 mode structure along with a harmonics m/n  =  2/2, which are coupled near the q  =  1 surface with a rotation frequency of 12 kHz.

  6. Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

    Science.gov (United States)

    Michelioudakis, Dimitrios G.; Hobbs, Richard W.; Caiado, Camila C. S.

    2018-03-01

    Estimating the depths of target horizons from seismic reflection data is an important task in exploration geophysics. To constrain these depths we need a reliable and accurate velocity model. Here, we build an optimum 2D seismic reflection data processing flow focused on pre - stack deghosting filters and velocity model building and apply Bayesian methods, including Gaussian process emulation and Bayesian History Matching (BHM), to estimate the uncertainties of the depths of key horizons near the borehole DSDP-258 located in the Mentelle Basin, south west of Australia, and compare the results with the drilled core from that well. Following this strategy, the tie between the modelled and observed depths from DSDP-258 core was in accordance with the ± 2σ posterior credibility intervals and predictions for depths to key horizons were made for the two new drill sites, adjacent the existing borehole of the area. The probabilistic analysis allowed us to generate multiple realizations of pre-stack depth migrated images, these can be directly used to better constrain interpretation and identify potential risk at drill sites. The method will be applied to constrain the drilling targets for the upcoming International Ocean Discovery Program (IODP), leg 369.

  7. Capturing cognitive causal paths in human reliability analysis with Bayesian network models

    International Nuclear Information System (INIS)

    Zwirglmaier, Kilian; Straub, Daniel; Groth, Katrina M.

    2017-01-01

    reIn the last decade, Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis (HRA), with multiple advantages over traditional HRA methods. In this paper we illustrate how BNs can be used to include additional, qualitative causal paths to provide traceability. The proposed framework provides the foundation to resolve several needs frequently expressed by the HRA community. First, the developed extended BN structure reflects the causal paths found in cognitive psychology literature, thereby addressing the need for causal traceability and strong scientific basis in HRA. Secondly, the use of node reduction algorithms allows the BN to be condensed to a level of detail at which quantification is as straightforward as the techniques used in existing HRA. We illustrate the framework by developing a BN version of the critical data misperceived crew failure mode in the IDHEAS HRA method, which is currently under development at the US NRC . We illustrate how the model could be quantified with a combination of expert-probabilities and information from operator performance databases such as SACADA. This paper lays the foundations necessary to expand the cognitive and quantitative foundations of HRA. - Highlights: • A framework for building traceable BNs for HRA, based on cognitive causal paths. • A qualitative BN structure, directly showing these causal paths is developed. • Node reduction algorithms are used for making the BN structure quantifiable. • BN quantified through expert estimates and observed data (Bayesian updating). • The framework is illustrated for a crew failure mode of IDHEAS.

  8. Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences.

    Science.gov (United States)

    Constantinou, Anthony Costa; Yet, Barbaros; Fenton, Norman; Neil, Martin; Marsh, William

    2016-01-01

    Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Rational hypocrisy: a Bayesian analysis based on informal argumentation and slippery slopes.

    Science.gov (United States)

    Rai, Tage S; Holyoak, Keith J

    2014-01-01

    Moral hypocrisy is typically viewed as an ethical accusation: Someone is applying different moral standards to essentially identical cases, dishonestly claiming that one action is acceptable while otherwise equivalent actions are not. We suggest that in some instances the apparent logical inconsistency stems from different evaluations of a weak argument, rather than dishonesty per se. Extending Corner, Hahn, and Oaksford's (2006) analysis of slippery slope arguments, we develop a Bayesian framework in which accusations of hypocrisy depend on inferences of shared category membership between proposed actions and previous standards, based on prior probabilities that inform the strength of competing hypotheses. Across three experiments, we demonstrate that inferences of hypocrisy increase as perceptions of the likelihood of shared category membership between precedent cases and current cases increase, that these inferences follow established principles of category induction, and that the presence of self-serving motives increases inferences of hypocrisy independent of changes in the actions themselves. Taken together, these results demonstrate that Bayesian analyses of weak arguments may have implications for assessing moral reasoning. © 2014 Cognitive Science Society, Inc.

  10. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.

    Science.gov (United States)

    Mo, Qianxing; Shen, Ronglai; Guo, Cui; Vannucci, Marina; Chan, Keith S; Hilsenbeck, Susan G

    2018-01-01

    Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  11. Bayesian bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximations.

    Science.gov (United States)

    Paul, M; Riebler, A; Bachmann, L M; Rue, H; Held, L

    2010-05-30

    For bivariate meta-analysis of diagnostic studies, likelihood approaches are very popular. However, they often run into numerical problems with possible non-convergence. In addition, the construction of confidence intervals is controversial. Bayesian methods based on Markov chain Monte Carlo (MCMC) sampling could be used, but are often difficult to implement, and require long running times and diagnostic convergence checks. Recently, a new Bayesian deterministic inference approach for latent Gaussian models using integrated nested Laplace approximations (INLA) has been proposed. With this approach MCMC sampling becomes redundant as the posterior marginal distributions are directly and accurately approximated. By means of a real data set we investigate the influence of the prior information provided and compare the results obtained by INLA, MCMC, and the maximum likelihood procedure SAS PROC NLMIXED. Using a simulation study we further extend the comparison of INLA and SAS PROC NLMIXED by assessing their performance in terms of bias, mean-squared error, coverage probability, and convergence rate. The results indicate that INLA is more stable and gives generally better coverage probabilities for the pooled estimates and less biased estimates of variance parameters. The user-friendliness of INLA is demonstrated by documented R-code. Copyright (c) 2010 John Wiley & Sons, Ltd.

  12. Fast Bayesian whole-brain fMRI analysis with spatial 3D priors.

    Science.gov (United States)

    Sidén, Per; Eklund, Anders; Bolin, David; Villani, Mattias

    2017-02-01

    Spatial whole-brain Bayesian modeling of task-related functional magnetic resonance imaging (fMRI) is a great computational challenge. Most of the currently proposed methods therefore do inference in subregions of the brain separately or do approximate inference without comparison to the true posterior distribution. A popular such method, which is now the standard method for Bayesian single subject analysis in the SPM software, is introduced in Penny et al. (2005b). The method processes the data slice-by-slice and uses an approximate variational Bayes (VB) estimation algorithm that enforces posterior independence between activity coefficients in different voxels. We introduce a fast and practical Markov chain Monte Carlo (MCMC) scheme for exact inference in the same model, both slice-wise and for the whole brain using a 3D prior on activity coefficients. The algorithm exploits sparsity and uses modern techniques for efficient sampling from high-dimensional Gaussian distributions, leading to speed-ups without which MCMC would not be a practical option. Using MCMC, we are for the first time able to evaluate the approximate VB posterior against the exact MCMC posterior, and show that VB can lead to spurious activation. In addition, we develop an improved VB method that drops the assumption of independent voxels a posteriori. This algorithm is shown to be much faster than both MCMC and the original VB for large datasets, with negligible error compared to the MCMC posterior. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Bayesian nonparametric estimation of continuous monotone functions with applications to dose-response analysis.

    Science.gov (United States)

    Bornkamp, Björn; Ickstadt, Katja

    2009-03-01

    In this article, we consider monotone nonparametric regression in a Bayesian framework. The monotone function is modeled as a mixture of shifted and scaled parametric probability distribution functions, and a general random probability measure is assumed as the prior for the mixing distribution. We investigate the choice of the underlying parametric distribution function and find that the two-sided power distribution function is well suited both from a computational and mathematical point of view. The model is motivated by traditional nonlinear models for dose-response analysis, and provides possibilities to elicitate informative prior distributions on different aspects of the curve. The method is compared with other recent approaches to monotone nonparametric regression in a simulation study and is illustrated on a data set from dose-response analysis.

  14. Bayesian analysis for exponential random graph models using the adaptive exchange sampler

    KAUST Repository

    Jin, Ick Hoon

    2013-01-01

    Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the existence of intractable normalizing constants. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the issue of intractable normalizing constants encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

  15. Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables

    DEFF Research Database (Denmark)

    Burgess, Stephen; Thompson, Simon G; Thompson, Grahame

    2010-01-01

    Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context...... of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable....... This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides...

  16. Multi-Objective data analysis using Bayesian Inference for MagLIF experiments

    Science.gov (United States)

    Knapp, Patrick; Glinksy, Michael; Evans, Matthew; Gom, Matth; Han, Stephanie; Harding, Eric; Slutz, Steve; Hahn, Kelly; Harvey-Thompson, Adam; Geissel, Matthias; Ampleford, David; Jennings, Christopher; Schmit, Paul; Smith, Ian; Schwarz, Jens; Peterson, Kyle; Jones, Brent; Rochau, Gregory; Sinars, Daniel

    2017-10-01

    The MagLIF concept has recently demonstrated Gbar pressures and confinement of charged fusion products at stagnation. We present a new analysis methodology that allows for integration of multiple diagnostics including nuclear, x-ray imaging, and x-ray power to determine the temperature, pressure, liner areal density, and mix fraction. A simplified hot-spot model is used with a Bayesian inference network to determine the most probable model parameters that describe the observations while simultaneously revealing the principal uncertainties in the analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.

  17. An efficient Bayesian data-worth analysis using a multilevel Monte Carlo method

    Science.gov (United States)

    Lu, Dan; Ricciuto, Daniel; Evans, Katherine

    2018-03-01

    Improving the understanding of subsurface systems and thus reducing prediction uncertainty requires collection of data. As the collection of subsurface data is costly, it is important that the data collection scheme is cost-effective. Design of a cost-effective data collection scheme, i.e., data-worth analysis, requires quantifying model parameter, prediction, and both current and potential data uncertainties. Assessment of these uncertainties in large-scale stochastic subsurface hydrological model simulations using standard Monte Carlo (MC) sampling or surrogate modeling is extremely computationally intensive, sometimes even infeasible. In this work, we propose an efficient Bayesian data-worth analysis using a multilevel Monte Carlo (MLMC) method. Compared to the standard MC that requires a significantly large number of high-fidelity model executions to achieve a prescribed accuracy in estimating expectations, the MLMC can substantially reduce computational costs using multifidelity approximations. Since the Bayesian data-worth analysis involves a great deal of expectation estimation, the cost saving of the MLMC in the assessment can be outstanding. While the proposed MLMC-based data-worth analysis is broadly applicable, we use it for a highly heterogeneous two-phase subsurface flow simulation to select an optimal candidate data set that gives the largest uncertainty reduction in predicting mass flow rates at four production wells. The choices made by the MLMC estimation are validated by the actual measurements of the potential data, and consistent with the standard MC estimation. But compared to the standard MC, the MLMC greatly reduces the computational costs.

  18. Antiplatelets versus anticoagulants for the treatment of cervical artery dissection: Bayesian meta-analysis.

    Directory of Open Access Journals (Sweden)

    Hakan Sarikaya

    Full Text Available To compare the effects of antiplatelets and anticoagulants on stroke and death in patients with acute cervical artery dissection.Systematic review with Bayesian meta-analysis.The reviewers searched MEDLINE and EMBASE from inception to November 2012, checked reference lists, and contacted authors.Studies were eligible if they were randomised, quasi-randomised or observational comparisons of antiplatelets and anticoagulants in patients with cervical artery dissection.Data were extracted by one reviewer and checked by another. Bayesian techniques were used to appropriately account for studies with scarce event data and imbalances in the size of comparison groups.Thirty-seven studies (1991 patients were included. We found no randomised trial. The primary analysis revealed a large treatment effect in favour of antiplatelets for preventing the primary composite outcome of ischaemic stroke, intracranial haemorrhage or death within the first 3 months after treatment initiation (relative risk 0.32, 95% credibility interval 0.12 to 0.63, while the degree of between-study heterogeneity was moderate (τ(2 = 0.18. In an analysis restricted to studies of higher methodological quality, the possible advantage of antiplatelets over anticoagulants was less obvious than in the main analysis (relative risk 0.73, 95% credibility interval 0.17 to 2.30.In view of these results and the safety advantages, easier usage and lower cost of antiplatelets, we conclude that antiplatelets should be given precedence over anticoagulants as a first line treatment in patients with cervical artery dissection unless results of an adequately powered randomised trial suggest the opposite.

  19. How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

    Science.gov (United States)

    Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

    2016-01-01

    Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

  20. Statistical Analysis of Firearms/Toolmarks Interpretation of Cartridge Case Evidence Using IBIS and Bayesian Networks

    Science.gov (United States)

    2015-10-24

    Taroni, F, Aitken, C, Garbolino, P, Biedermann, A, Bayesian Networks and Probabilistic Inference in Forensic Science (Statistics in Practice), Wiley...were transformed into a Bayesian network . Bayesian networks allow for the assessment of evidence based upon two propositions (same gun or different...gun). This allows a forensic scientist to provide insight to courts and investigators as to the value of the evidence. The breech face (BF) and

  1. Failure rate modeling using fault tree analysis and Bayesian network: DEMO pulsed operation turbine study case

    International Nuclear Information System (INIS)

    Dongiovanni, Danilo Nicola; Iesmantas, Tomas

    2016-01-01

    Highlights: • RAMI (Reliability, Availability, Maintainability and Inspectability) assessment of secondary heat transfer loop for a DEMO nuclear fusion plant. • Definition of a fault tree for a nuclear steam turbine operated in pulsed mode. • Turbine failure rate models update by mean of a Bayesian network reflecting the fault tree analysis in the considered scenario. • Sensitivity analysis on system availability performance. - Abstract: Availability will play an important role in the Demonstration Power Plant (DEMO) success from an economic and safety perspective. Availability performance is commonly assessed by Reliability Availability Maintainability Inspectability (RAMI) analysis, strongly relying on the accurate definition of system components failure modes (FM) and failure rates (FR). Little component experience is available in fusion application, therefore requiring the adaptation of literature FR to fusion plant operating conditions, which may differ in several aspects. As a possible solution to this problem, a new methodology to extrapolate/estimate components failure rate under different operating conditions is presented. The DEMO Balance of Plant nuclear steam turbine component operated in pulse mode is considered as study case. The methodology moves from the definition of a fault tree taking into account failure modes possibly enhanced by pulsed operation. The fault tree is then translated into a Bayesian network. A statistical model for the turbine system failure rate in terms of subcomponents’ FR is hence obtained, allowing for sensitivity analyses on the structured mixture of literature and unknown FR data for which plausible value intervals are investigated to assess their impact on the whole turbine system FR. Finally, the impact of resulting turbine system FR on plant availability is assessed exploiting a Reliability Block Diagram (RBD) model for a typical secondary cooling system implementing a Rankine cycle. Mean inherent availability

  2. Amiodarone, lidocaine, magnesium or placebo in shock refractory ventricular arrhythmia: A Bayesian network meta-analysis.

    Science.gov (United States)

    Khan, Safi U; Winnicka, Lydia; Saleem, Muhammad A; Rahman, Hammad; Rehman, Najeeb

    Recent evidence challenges, the superiority of amiodarone, compared to other anti-arrhythmic medications, as the agent of choice in pulseless ventricular tachycardia (VT) or ventricular fibrillation (VF). We conducted Bayesian network and traditional meta-analyses to investigate the relative efficacies of amiodarone, lidocaine, magnesium (MgSO4) and placebo as treatments for pulseless VT or VF. Eleven studies [5200 patients, 7 randomized trials (4, 611 patients) and 4 non-randomized studies (589 patients)], were included in this meta-analysis. The search was conducted, from 1981 to February 2017, using MEDLINE, EMBASE and The Cochrane Library. Estimates were reported as odds ratio (OR) with 95% Credible Interval (CrI). Markov chain Monte Carlo (MCMC) modeling was used to estimate the relative ranking probability of each treatment group based on surface under cumulative ranking curve (SUCRA). Bayesian analysis demonstrated that lidocaine had superior effects on survival to hospital discharge, compared to amiodarone (OR, 2.18, 95% Cr.I 1.26-3.13), MgSO4 (OR, 2.03, 95% Cr.I 0.74-4.82) and placebo (OR, 2.42, 95% Cr.I 1.39-3.54). There were no statistical differences among treatment groups regarding survival to hospital admission/24 h (hrs) and return of spontaneous circulation (ROSC). Probability analysis revealed that lidocaine was the most effective therapy for survival to hospital discharge (SUCRA, 97%). We conclude that lidocaine may be the most effective anti-arrhythmic agent for survival to hospital discharge in patients with pulseless VT or VF. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Bayesian Networks for the Age Classification of Living Individuals: A Study on Transition Analysis

    Directory of Open Access Journals (Sweden)

    Emanuele Sironi

    2015-01-01

    Full Text Available Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.

  4. Failure rate modeling using fault tree analysis and Bayesian network: DEMO pulsed operation turbine study case

    Energy Technology Data Exchange (ETDEWEB)

    Dongiovanni, Danilo Nicola, E-mail: danilo.dongiovanni@enea.it [ENEA, Nuclear Fusion and Safety Technologies Department, via Enrico Fermi 45, Frascati 00040 (Italy); Iesmantas, Tomas [LEI, Breslaujos str. 3 Kaunas (Lithuania)

    2016-11-01

    Highlights: • RAMI (Reliability, Availability, Maintainability and Inspectability) assessment of secondary heat transfer loop for a DEMO nuclear fusion plant. • Definition of a fault tree for a nuclear steam turbine operated in pulsed mode. • Turbine failure rate models update by mean of a Bayesian network reflecting the fault tree analysis in the considered scenario. • Sensitivity analysis on system availability performance. - Abstract: Availability will play an important role in the Demonstration Power Plant (DEMO) success from an economic and safety perspective. Availability performance is commonly assessed by Reliability Availability Maintainability Inspectability (RAMI) analysis, strongly relying on the accurate definition of system components failure modes (FM) and failure rates (FR). Little component experience is available in fusion application, therefore requiring the adaptation of literature FR to fusion plant operating conditions, which may differ in several aspects. As a possible solution to this problem, a new methodology to extrapolate/estimate components failure rate under different operating conditions is presented. The DEMO Balance of Plant nuclear steam turbine component operated in pulse mode is considered as study case. The methodology moves from the definition of a fault tree taking into account failure modes possibly enhanced by pulsed operation. The fault tree is then translated into a Bayesian network. A statistical model for the turbine system failure rate in terms of subcomponents’ FR is hence obtained, allowing for sensitivity analyses on the structured mixture of literature and unknown FR data for which plausible value intervals are investigated to assess their impact on the whole turbine system FR. Finally, the impact of resulting turbine system FR on plant availability is assessed exploiting a Reliability Block Diagram (RBD) model for a typical secondary cooling system implementing a Rankine cycle. Mean inherent availability

  5. Current trends in Bayesian methodology with applications

    CERN Document Server

    Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia

    2015-01-01

    Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on

  6. A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

    Science.gov (United States)

    Karabatsos, George

    2017-02-01

    Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected

  7. Bayesian analysis of the effective charge from spectroscopic bremsstrahlung measurement in fusion plasmas

    Science.gov (United States)

    Krychowiak, M.; König, R.; Klinger, T.; Fischer, R.

    2004-11-01

    At the stellarator Wendelstein 7-AS (W7-AS) a spectrally resolving two channel system for the measurement of line-of-sight averaged Zeff values has been tested in preparation for its planned installation as a multichannel Zeff-profile measurement system on the stellarator Wendelstein 7-X (W7-X) which is presently under construction. The measurement is performed using the bremsstrahlung intensity in the wavelength region of ultraviolet to near infrared. The spectrally resolved measurement allows to eliminate signal contamination by line radiation. For statistical data analysis a procedure based on Bayesian probability theory has been developed. With this method it is possible to estimate the bremsstrahlung background in the measured signal and its error without the necessity to fit the spectral lines. For evaluation of the random error in Zeff the signal noise has been investigated. Furthermore, the linearity and behavior of the charge-coupled device detector at saturation has been analyzed.

  8. A Bayesian approach to probabilistic sensitivity analysis in structured benefit-risk assessment.

    Science.gov (United States)

    Waddingham, Ed; Mt-Isa, Shahrul; Nixon, Richard; Ashby, Deborah

    2016-01-01

    Quantitative decision models such as multiple criteria decision analysis (MCDA) can be used in benefit-risk assessment to formalize trade-offs between benefits and risks, providing transparency to the assessment process. There is however no well-established method for propagating uncertainty of treatment effects data through such models to provide a sense of the variability of the benefit-risk balance. Here, we present a Bayesian statistical method that directly models the outcomes observed in randomized placebo-controlled trials and uses this to infer indirect comparisons between competing active treatments. The resulting treatment effects estimates are suitable for use within the MCDA setting, and it is possible to derive the distribution of the overall benefit-risk balance through Markov Chain Monte Carlo simulation. The method is illustrated using a case study of natalizumab for relapsing-remitting multiple sclerosis. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank.

    Science.gov (United States)

    Cortes, Adrian; Dendrou, Calliope A; Motyer, Allan; Jostins, Luke; Vukcevic, Damjan; Dilthey, Alexander; Donnelly, Peter; Leslie, Stephen; Fugger, Lars; McVean, Gil

    2017-09-01

    Genetic discovery from the multitude of phenotypes extractable from routine healthcare data can transform understanding of the human phenome and accelerate progress toward precision medicine. However, a critical question when analyzing high-dimensional and heterogeneous data is how best to interrogate increasingly specific subphenotypes while retaining statistical power to detect genetic associations. Here we develop and employ a new Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to analyze genetic variants against UK Biobank disease phenotypes derived from self-reporting and hospital episode statistics. Our method displays a more than 20% increase in power to detect genetic effects over other approaches and identifies new associations between classical human leukocyte antigen (HLA) alleles and common immune-mediated diseases (IMDs). By applying the approach to genetic risk scores (GRSs), we show the extent of genetic sharing among IMDs and expose differences in disease perception or diagnosis with potential clinical implications.

  10. Bayesian Reliability Analysis of Non-Stationarity in Multi-agent Systems

    Directory of Open Access Journals (Sweden)

    TONT Gabriela

    2013-05-01

    Full Text Available The Bayesian methods provide information about the meaningful parameters in a statistical analysis obtained by combining the prior and sampling distributions to form the posterior distribution of theparameters. The desired inferences are obtained from this joint posterior. An estimation strategy for hierarchical models, where the resulting joint distribution of the associated model parameters cannotbe evaluated analytically, is to use sampling algorithms, known as Markov Chain Monte Carlo (MCMC methods, from which approximate solutions can be obtained. Both serial and parallel configurations of subcomponents are permitted. The capability of time-dependent method to describe a multi-state system is based on a case study, assessingthe operatial situation of studied system. The rationality and validity of the presented model are demonstrated via a case of study. The effect of randomness of the structural parameters is alsoexamined.

  11. Bayesian operational modal analysis with asynchronous data, part I: Most probable value

    Science.gov (United States)

    Zhu, Yi-Chen; Au, Siu-Kui

    2018-01-01

    In vibration tests, multiple sensors are used to obtain detailed mode shape information about the tested structure. Time synchronisation among data channels is required in conventional modal identification approaches. Modal identification can be more flexibly conducted if this is not required. Motivated by the potential gain in feasibility and economy, this work proposes a Bayesian frequency domain method for modal identification using asynchronous 'output-only' ambient data, i.e. 'operational modal analysis'. It provides a rigorous means for identifying the global mode shape taking into account the quality of the measured data and their asynchronous nature. This paper (Part I) proposes an efficient algorithm for determining the most probable values of modal properties. The method is validated using synthetic and laboratory data. The companion paper (Part II) investigates identification uncertainty and challenges in applications to field vibration data.

  12. Analytic Bayesian solution of the two-stage poisson-type problem in probabilistic risk analysis

    International Nuclear Information System (INIS)

    Frohner, F.H.

    1985-01-01

    The basic purpose of probabilistic risk analysis is to make inferences about the probabilities of various postulated events, with an account of all relevant information such as prior knowledge and operating experience with the specific system under study, as well as experience with other similar systems. Estimation of the failure rate of a Poisson-type system leads to an especially simple Bayesian solution in closed form if the prior probabilty implied by the invariance properties of the problem is properly taken into account. This basic simplicity persists if a more realistic prior, representing order of magnitude knowledge of the rate parameter, is employed instead. Moreover, the more realistic prior allows direct incorporation of experience gained from other similar systems, without need to postulate a statistical model for an underlying ensemble. The analytic formalism is applied to actual nuclear reactor data

  13. Bayesian Analysis of Multidimensional Item Response Theory Models: A Discussion and Illustration of Three Response Style Models

    Science.gov (United States)

    Leventhal, Brian C.; Stone, Clement A.

    2018-01-01

    Interest in Bayesian analysis of item response theory (IRT) models has grown tremendously due to the appeal of the paradigm among psychometricians, advantages of these methods when analyzing complex models, and availability of general-purpose software. Possible models include models which reflect multidimensionality due to designed test structure,…

  14. Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives

    NARCIS (Netherlands)

    Durbin, J.; Koopman, S.J.M.

    1998-01-01

    The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian

  15. PFG NMR and Bayesian analysis to characterise non-Newtonian fluids

    Science.gov (United States)

    Blythe, Thomas W.; Sederman, Andrew J.; Stitt, E. Hugh; York, Andrew P. E.; Gladden, Lynn F.

    2017-01-01

    Many industrial flow processes are sensitive to changes in the rheological behaviour of process fluids, and there therefore exists a need for methods that provide online, or inline, rheological characterisation necessary for process control and optimisation over timescales of minutes or less. Nuclear magnetic resonance (NMR) offers a non-invasive technique for this application, without limitation on optical opacity. We present a Bayesian analysis approach using pulsed field gradient (PFG) NMR to enable estimation of the rheological parameters of Herschel-Bulkley fluids in a pipe flow geometry, characterised by a flow behaviour index n , yield stress τ0 , and consistency factor k , by analysis of the signal in q -space. This approach eliminates the need for velocity image acquisition and expensive gradient hardware. We investigate the robustness of the proposed Bayesian NMR approach to noisy data and reduced sampling using simulated NMR data and show that even with a signal-to-noise ratio (SNR) of 100, only 16 points are required to be sampled to provide rheological parameters accurate to within 2% of the ground truth. Experimental validation is provided through an experimental case study on Carbopol 940 solutions (model Herschel-Bulkley fluids) using PFG NMR at a 1H resonance frequency of 85.2 MHz; for SNR > 1000, only 8 points are required to be sampled. This corresponds to a total acquisition time of probably due to shear history-dependent behaviour and the different geometries used. This behaviour highlights the need for online, or inline, rheological characterisation in industrial process applications.

  16. A Bayesian-based multilevel factorial analysis method for analyzing parameter uncertainty of hydrological model

    Science.gov (United States)

    Liu, Y. R.; Li, Y. P.; Huang, G. H.; Zhang, J. L.; Fan, Y. R.

    2017-10-01

    In this study, a Bayesian-based multilevel factorial analysis (BMFA) method is developed to assess parameter uncertainties and their effects on hydrological model responses. In BMFA, Differential Evolution Adaptive Metropolis (DREAM) algorithm is employed to approximate the posterior distributions of model parameters with Bayesian inference; factorial analysis (FA) technique is used for measuring the specific variations of hydrological responses in terms of posterior distributions to investigate the individual and interactive effects of parameters on model outputs. BMFA is then applied to a case study of the Jinghe River watershed in the Loess Plateau of China to display its validity and applicability. The uncertainties of four sensitive parameters, including soil conservation service runoff curve number to moisture condition II (CN2), soil hydraulic conductivity (SOL_K), plant available water capacity (SOL_AWC), and soil depth (SOL_Z), are investigated. Results reveal that (i) CN2 has positive effect on peak flow, implying that the concentrated rainfall during rainy season can cause infiltration-excess surface flow, which is an considerable contributor to peak flow in this watershed; (ii) SOL_K has positive effect on average flow, implying that the widely distributed cambisols can lead to medium percolation capacity; (iii) the interaction between SOL_AWC and SOL_Z has noticeable effect on the peak flow and their effects are dependent upon each other, which discloses that soil depth can significant influence the processes of plant uptake of soil water in this watershed. Based on the above findings, the significant parameters and the relationship among uncertain parameters can be specified, such that hydrological model's capability for simulating/predicting water resources of the Jinghe River watershed can be improved.

  17. From "weight of evidence" to quantitative data integration using multicriteria decision analysis and Bayesian methods.

    Science.gov (United States)

    Linkov, Igor; Massey, Olivia; Keisler, Jeff; Rusyn, Ivan; Hartung, Thomas

    2015-01-01

    "Weighing" available evidence in the process of decision-making is unavoidable, yet it is one step that routinely raises suspicions: what evidence should be used, how much does it weigh, and whose thumb may be tipping the scales? This commentary aims to evaluate the current state and future roles of various types of evidence for hazard assessment as it applies to environmental health. In its recent evaluation of the US Environmental Protection Agency's Integrated Risk Information System assessment process, the National Research Council committee singled out the term "weight of evidence" (WoE) for critique, deeming the process too vague and detractive to the practice of evaluating human health risks of chemicals. Moving the methodology away from qualitative, vague and controversial methods towards generalizable, quantitative and transparent methods for appropriately managing diverse lines of evidence is paramount for both regulatory and public acceptance of the hazard assessments. The choice of terminology notwithstanding, a number of recent Bayesian WoE-based methods, the emergence of multi criteria decision analysis for WoE applications, as well as the general principles behind the foundational concepts of WoE, show promise in how to move forward and regain trust in the data integration step of the assessments. We offer our thoughts on the current state of WoE as a whole and while we acknowledge that many WoE applications have been largely qualitative and subjective in nature, we see this as an opportunity to turn WoE towards a quantitative direction that includes Bayesian and multi criteria decision analysis.

  18. BGX: a Bioconductor package for the Bayesian integrated analysis of Affymetrix GeneChips

    Directory of Open Access Journals (Sweden)

    Hein Anne-Mette K

    2007-11-01

    Full Text Available Abstract Background Affymetrix 3' GeneChip microarrays are widely used to profile the expression of thousands of genes simultaneously. They differ from many other microarray types in that GeneChips are hybridised using a single labelled extract and because they contain multiple 'match' and 'mismatch' sequences for each transcript. Most algorithms extract the signal from GeneChip experiments in a sequence of separate steps, including background correction and normalisation, which inhibits the simultaneous use of all available information. They principally provide a point estimate of gene expression and, in contrast to BGX, do not fully integrate the uncertainty arising from potentially heterogeneous responses of the probes. Results BGX is a new Bioconductor R package that implements an integrated Bayesian approach to the analysis of 3' GeneChip data. The software takes into account additive and multiplicative error, non-specific hybridisation and replicate summarisation in the spirit of the model outlined in 1. It also provides a posterior distribution for the expression of each gene. Moreover, BGX can take into account probe affinity effects from probe sequence information where available. The package employs a novel adaptive Markov chain Monte Carlo (MCMC algorithm that raises considerably the efficiency with which the posterior distributions are sampled from. Finally, BGX incorporates various ways to analyse the results, such as ranking genes by expression level as well as statistically based methods for estimating the amount of up and down regulated genes between two conditions. Conclusion BGX performs well relative to other widely used methods at estimating expression levels and fold changes. It has the advantage that it provides a statistically sound measure of uncertainty for its estimates. BGX includes various analysis functions to visualise and exploit the rich output that is produced by the Bayesian model.

  19. Systemic antibiotics in the treatment of aggressive periodontitis. A systematic review and a Bayesian Network meta-analysis.

    Science.gov (United States)

    Rabelo, Cleverton Correa; Feres, Magda; Gonçalves, Cristiane; Figueiredo, Luciene C; Faveri, Marcelo; Tu, Yu-Kang; Chambrone, Leandro

    2015-07-01

    The aim of this study was to assess the effect of systemic antibiotic therapy on the treatment of aggressive periodontitis (AgP). This study was conducted and reported in accordance with the PRISMA statement. The MEDLINE, EMBASE and CENTRAL databases were searched up to June 2014 for randomized clinical trials comparing the treatment of subjects with AgP with either scaling and root planing (SRP) alone or associated with systemic antibiotics. Bayesian network meta-analysis was prepared using the Bayesian random-effects hierarchical models and the outcomes reported at 6-month post-treatment. Out of 350 papers identified, 14 studies were eligible. Greater gain in clinical attachment (CA) (mean difference [MD]: 1.08 mm; p < 0.0001) and reduction in probing depth (PD) (MD: 1.05 mm; p < 0.00001) were observed for SRP + metronidazole (Mtz), and for SRP + Mtz + amoxicillin (Amx) (MD: 0.45 mm, MD: 0.53 mm, respectively; p < 0.00001) than SRP alone/placebo. Bayesian network meta-analysis showed additional benefits in CA gain and PD reduction when SRP was associated with systemic antibiotics. SRP plus systemic antibiotics led to an additional clinical effect compared with SRP alone in the treatment of AgP. Of the antibiotic protocols available for inclusion into the Bayesian network meta-analysis, Mtz and Mtz/Amx provided to the most beneficial outcomes. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  20. Bayesian analysis of risk associated with workplace accidents in earthmoving operations

    Directory of Open Access Journals (Sweden)

    J. F. García

    2017-06-01

    Full Text Available This paper analyses the characteristics of earthmoving operations involving a workplace accident. Bayesian networks were used to identify the factors that best predicted potential risk situations. Inference studies were then conducted to analyse the interplay between different risk factors. We demonstrate the potential of Bayesian networks to describe workplace contexts and predict risk situations from a safety and production planning perspective.

  1. A Bayesian Approach to Person Fit Analysis in Item Response Theory Models. Research Report.

    Science.gov (United States)

    Glas, Cees A. W.; Meijer, Rob R.

    A Bayesian approach to the evaluation of person fit in item response theory (IRT) models is presented. In a posterior predictive check, the observed value on a discrepancy variable is positioned in its posterior distribution. In a Bayesian framework, a Markov Chain Monte Carlo procedure can be used to generate samples of the posterior distribution…

  2. Spectral analysis of the IntCal98 calibration curve: a Bayesian view

    International Nuclear Information System (INIS)

    Palonen, V.; Tikkanen, P.

    2004-01-01

    Preliminary results from a Bayesian approach to find periodicities in the IntCal98 calibration curve are given. It has been shown in the literature that the discrete Fourier transform (Schuster periodogram) corresponds to the use of an approximate Bayesian model of one harmonic frequency and Gaussian noise. Advantages of the Bayesian approach include the possibility to use models for variable, attenuated and multiple frequencies, the capability to analyze unevenly spaced data and the possibility to assess the significance and uncertainties of spectral estimates. In this work, a new Bayesian model using random walk noise to take care of the trend in the data is developed. Both Bayesian models are described and the first results of the new model are reported and compared with results from straightforward discrete-Fourier-transform and maximum-entropy-method spectral analyses

  3. Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures

    International Nuclear Information System (INIS)

    Khakzad, Nima

    2015-01-01

    A domino effect is a low frequency high consequence chain of accidents where a primary accident (usually fire and explosion) in a unit triggers secondary accidents in adjacent units. High complexity and growing interdependencies of chemical infrastructures make them increasingly vulnerable to domino effects. Domino effects can be considered as time dependent processes. Thus, not only the identification of involved units but also their temporal entailment in the chain of accidents matter. More importantly, in the case of domino-induced fires which can generally last much longer compared to explosions, foreseeing the temporal evolution of domino effects and, in particular, predicting the most probable sequence of accidents (or involved units) in a domino effect can be of significance in the allocation of preventive and protective safety measures. Although many attempts have been made to identify the spatial evolution of domino effects, the temporal evolution of such accidents has been overlooked. We have proposed a methodology based on dynamic Bayesian network to model both the spatial and temporal evolutions of domino effects and also to quantify the most probable sequence of accidents in a potential domino effect. The application of the developed methodology has been demonstrated via a hypothetical fuel storage plant. - Highlights: • A Dynamic Bayesian Network methodology has been developed to model domino effects. • Considering time-dependencies, both spatial and temporal evolutions of domino effects have been modeled. • The concept of most probable sequence of accidents has been proposed instead of the most probable combination of accidents. • Using backward analysis, the most vulnerable units have been identified during a potential domino effect. • The proposed methodology does not need to identify a unique primary unit (accident) for domino effect modeling

  4. Gamma prior distribution selection for Bayesian analysis of failure rate and reliability

    International Nuclear Information System (INIS)

    Waler, R.A.; Johnson, M.M.; Waterman, M.S.; Martz, H.F. Jr.

    1977-01-01

    It is assumed that the phenomenon under study is such that the time-to-failure may be modeled by an exponential distribution with failure-rate parameter, lambda. For Bayesian analyses of the assumed model, the family of gamma distributions provides conjugate prior models for lambda. Thus, an experimenter needs to select a particular gamma model to conduct a Bayesian reliability analysis. The purpose of this paper is to present a methodology which can be used to translate engineering information, experience, and judgment into a choice of a gamma prior distribution. The proposed methodology assumes that the practicing engineer can provide percentile data relating to either the failure rate or the reliability of the phenomenon being investigated. For example, the methodology will select the gamma prior distribution which conveys an engineer's belief that the failure rate, lambda, simultaneously satisfies the probability statements, P(lambda less than 1.0 x 10 -3 ) = 0.50 and P(lambda less than 1.0 x 10 -5 ) = 0.05. That is, two percentiles provided by an engineer are used to determine a gamma prior model which agrees with the specified percentiles. For those engineers who prefer to specify reliability percentiles rather than the failure-rate percentiles illustrated above, one can use the induced negative-log gamma prior distribution which satisfies the probability statements, P(R(t 0 ) less than 0.99) = 0.50 and P(R(t 0 ) less than 0.99999) = 0.95 for some operating time t 0 . Also, the paper includes graphs for selected percentiles which assist an engineer in applying the methodology

  5. Bayesian analysis of data and model error in rainfall-runoff hydrological models

    Science.gov (United States)

    Kavetski, D.; Franks, S. W.; Kuczera, G.

    2004-12-01

    A major unresolved issue in the identification and use of conceptual hydrologic models is realistic description of uncertainty in the data and model structure. In particular, hydrologic parameters often cannot be measured directly and must be inferred (calibrated) from observed forcing/response data (typically, rainfall and runoff). However, rainfall varies significantly in space and time, yet is often estimated from sparse gauge networks. Recent work showed that current calibration methods (e.g., standard least squares, multi-objective calibration, generalized likelihood uncertainty estimation) ignore forcing uncertainty and assume that the rainfall is known exactly. Consequently, they can yield strongly biased and misleading parameter estimates. This deficiency confounds attempts to reliably test model hypotheses, to generalize results across catchments (the regionalization problem) and to quantify predictive uncertainty when the hydrologic model is extrapolated. This paper continues the development of a Bayesian total error analysis (BATEA) methodology for the calibration and identification of hydrologic models, which explicitly incorporates the uncertainty in both the forcing and response data, and allows systematic model comparison based on residual model errors and formal Bayesian hypothesis testing (e.g., using Bayes factors). BATEA is based on explicit stochastic models for both forcing and response uncertainty, whereas current techniques focus solely on response errors. Hence, unlike existing methods, the BATEA parameter equations directly reflect the modeler's confidence in all the data. We compare several approaches to approximating the parameter distributions: a) full Markov Chain Monte Carlo methods and b) simplified approaches based on linear approximations. Studies using synthetic and real data from the US and Australia show that BATEA systematically reduces the parameter bias, leads to more meaningful model fits and allows model comparison taking

  6. Bias correction and Bayesian analysis of aggregate counts in SAGE libraries

    Directory of Open Access Journals (Sweden)

    Briggs William M

    2010-02-01

    Full Text Available Abstract Background Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism's transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3' most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power. Results Three new hierarchical models, which directly embed a model for the variation in tag formation probability, are proposed and their associated Bayesian inference algorithms are developed. These models may be applied to libraries at both the tag and aggregate level. Simulation experiments and analysis of real data are used to contrast the accuracy of the various methods. The consequences of tag formation bias are discussed in the context of testing differential expression. A description is given as to how these algorithms can be applied in that context. Conclusions Several Bayesian inference algorithms that account for tag formation effects are compared with the DPB algorithm providing clear evidence of superior performance. The accuracy of inferences when using a particular non-informative prior is found to depend on the expression level of a given gene. The multivariate nature of the approach easily allows both univariate and joint tests of differential expression. Calculations demonstrate the potential for false positive and negative findings due to variation in tag formation probabilities across samples when testing for differential expression.

  7. Bayesian adaptive methods for clinical trials

    CERN Document Server

    Berry, Scott M; Muller, Peter

    2010-01-01

    Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISP...

  8. A Bayesian network meta-analysis of whole brain radiotherapy and stereotactic radiotherapy for brain metastasis.

    Science.gov (United States)

    Yuan, Xi; Liu, Wen-Jie; Li, Bing; Shen, Ze-Tian; Shen, Jun-Shu; Zhu, Xi-Xu

    2017-08-01

    This study was conducted to compare the effects of whole brain radiotherapy (WBRT) and stereotactic radiotherapy (SRS) in treatment of brain metastasis.A systematical retrieval in PubMed and Embase databases was performed for relative literatures on the effects of WBRT and SRS in treatment of brain metastasis. A Bayesian network meta-analysis was performed by using the ADDIS software. The effect sizes included odds ratio (OR) and 95% confidence interval (CI). A random effects model was used for the pooled analysis for all the outcome measures, including 1-year distant control rate, 1-year local control rate, 1-year survival rate, and complication. The consistency was tested by using node-splitting analysis and inconsistency standard deviation. The convergence was estimated according to the Brooks-Gelman-Rubin method.A total of 12 literatures were included in this meta-analysis. WBRT + SRS showed higher 1-year distant control rate than SRS. WBRT + SRS was better for the 1-year local control rate than WBRT. SRS and WBRT + SRS had higher 1-year survival rate than the WBRT. In addition, there was no difference in complication among the three therapies.Comprehensively, WBRT + SRS might be the choice of treatment for brain metastasis.

  9. The Phylogeographic History of the New World Screwworm Fly, Inferred by Approximate Bayesian Computation Analysis

    Science.gov (United States)

    Azeredo-Espin, Ana Maria L.

    2013-01-01

    Insect pest phylogeography might be shaped both by biogeographic events and by human influence. Here, we conducted an approximate Bayesian computation (ABC) analysis to investigate the phylogeography of the New World screwworm fly, Cochliomyia hominivorax, with the aim of understanding its population history and its order and time of divergence. Our ABC analysis supports that populations spread from North to South in the Americas, in at least two different moments. The first split occurred between the North/Central American and South American populations in the end of the Last Glacial Maximum (15,300-19,000 YBP). The second split occurred between the North and South Amazonian populations in the transition between the Pleistocene and the Holocene eras (9,100-11,000 YBP). The species also experienced population expansion. Phylogenetic analysis likewise suggests this north to south colonization and Maxent models suggest an increase in the number of suitable areas in South America from the past to present. We found that the phylogeographic patterns observed in C. hominivorax cannot be explained only by climatic oscillations and can be connected to host population histories. Interestingly we found these patterns are very coincident with general patterns of ancient human movements in the Americas, suggesting that humans might have played a crucial role in shaping the distribution and population structure of this insect pest. This work presents the first hypothesis test regarding the processes that shaped the current phylogeographic structure of C. hominivorax and represents an alternate perspective on investigating the problem of insect pests. PMID:24098436

  10. Analysis of human and organizational factors that influence mining accidents based on Bayesian network.

    Science.gov (United States)

    Mirzaei Aliabadi, Mostafa; Aghaei, Hamed; Kalatpour, Omid; Soltanian, Ali Reza; Nikravesh, Asghar

    2018-03-21

    The present study was aimed to analyze human and organizational factors involved in mining accidents and determine the relationships among these factors. In this study, Human Factors Analysis and Classification System (HFACS) with Bayesian network (BN) were combined in order to analyze contributing factors in mining accidents. BN was constructed based on a hierarchal structure of HFACS. The required data were collected from a total of 295 cases of Iranian mining accidents and analyzed using HFACS. Afterwards, prior probability of contributing factors was computed using the expectation-maximization algorithm. Sensitivity analysis was applied to determine which contributing factor had a higher influence on unsafe acts to select the best intervention strategy. The analyses showed that skill based errors, routine violations, environmental factors, and planned inappropriate operation had a higher relative importance in the accidents. Moreover, sensitivity analysis revealed that environmental factors, failed to correct known problem, and personnel factors had a higher influence on unsafe acts. The results of the present study could provide guidance to help safety and health management by adopting proper intervention strategies to reduce mining accidents.

  11. Integrated data analysis of fusion diagnostics by means of the Bayesian probability theory

    International Nuclear Information System (INIS)

    Fischer, R.; Dinklage, A.

    2004-01-01

    Integrated data analysis (IDA) of fusion diagnostics is the combination of heterogeneous diagnostics to obtain validated physical results. Benefits from the integrated approach result from a systematic use of interdependencies; in that sense IDA optimizes the extraction of information from sets of different data. For that purpose IDA requires a systematic and formalized error analysis of all (statistical and systematic) uncertainties involved in each diagnostic. Bayesian probability theory allows for a systematic combination of all information entering the diagnostic model by considering all uncertainties of the measured data, the calibration measurements, and the physical model. Prior physics knowledge on model parameters can be included. Handling of systematic errors is provided. A central goal of the integration of redundant or complementary diagnostics is to provide information to resolve inconsistencies by exploiting interdependencies. A comparable analysis of sets of diagnostics (meta-diagnostics) is performed by combining statistical and systematical uncertainties with model parameters and model uncertainties. Diagnostics improvement and experimental optimization and design of meta-diagnostics will be discussed

  12. Efficient Methods for Bayesian Uncertainty Analysis and Global Optimization of Computationally Expensive Environmental Models

    Science.gov (United States)

    Shoemaker, Christine; Espinet, Antoine; Pang, Min

    2015-04-01

    Models of complex environmental systems can be computationally expensive in order to describe the dynamic interactions of the many components over a sizeable time period. Diagnostics of these systems can include forward simulations of calibrated models under uncertainty and analysis of alternatives of systems management. This discussion will focus on applications of new surrogate optimization and uncertainty analysis methods to environmental models that can enhance our ability to extract information and understanding. For complex models, optimization and especially uncertainty analysis can require a large number of model simulations, which is not feasible for computationally expensive models. Surrogate response surfaces can be used in Global Optimization and Uncertainty methods to obtain accurate answers with far fewer model evaluations, which made the methods practical for computationally expensive models for which conventional methods are not feasible. In this paper we will discuss the application of the SOARS surrogate method for estimating Bayesian posterior density functions for model parameters for a TOUGH2 model of geologic carbon sequestration. We will also briefly discuss new parallel surrogate global optimization algorithm applied to two groundwater remediation sites that was implemented on a supercomputer with up to 64 processors. The applications will illustrate the use of these methods to predict the impact of monitoring and management on subsurface contaminants.

  13. Bayesian Inference for Neural Electromagnetic Source Localization: Analysis of MEG Visual Evoked Activity

    International Nuclear Information System (INIS)

    George, J.S.; Schmidt, D.M.; Wood, C.C.

    1999-01-01

    We have developed a Bayesian approach to the analysis of neural electromagnetic (MEG/EEG) data that can incorporate or fuse information from other imaging modalities and addresses the ill-posed inverse problem by sarnpliig the many different solutions which could have produced the given data. From these samples one can draw probabilistic inferences about regions of activation. Our source model assumes a variable number of variable size cortical regions of stimulus-correlated activity. An active region consists of locations on the cortical surf ace, within a sphere centered on some location in cortex. The number and radi of active regions can vary to defined maximum values. The goal of the analysis is to determine the posterior probability distribution for the set of parameters that govern the number, location, and extent of active regions. Markov Chain Monte Carlo is used to generate a large sample of sets of parameters distributed according to the posterior distribution. This sample is representative of the many different source distributions that could account for given data, and allows identification of probable (i.e. consistent) features across solutions. Examples of the use of this analysis technique with both simulated and empirical MEG data are presented

  14. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis

    Science.gov (United States)

    Down, Thomas A.; Rakyan, Vardhman K.; Turner, Daniel J.; Flicek, Paul; Li, Heng; Kulesha, Eugene; Gräf, Stefan; Johnson, Nathan; Herrero, Javier; Tomazou, Eleni M.; Thorne, Natalie P.; Bäckdahl, Liselotte; Herberth, Marlis; Howe, Kevin L.; Jackson, David K.; Miretti, Marcos M.; Marioni, John C.; Birney, Ewan; Hubbard, Tim J. P.; Durbin, Richard; Tavaré, Simon; Beck, Stephan

    2009-01-01

    DNA methylation is an indispensible epigenetic modification of mammalian genomes. Consequently there is great interest in strategies for genome-wide/whole-genome DNA methylation analysis, and immunoprecipitation-based methods have proven to be a powerful option. Such methods are rapidly shifting the bottleneck from data generation to data analysis, necessitating the development of better analytical tools. Until now, a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling has been the inability to estimate absolute methylation levels. Here we report the development of a novel cross-platform algorithm – Bayesian Tool for Methylation Analysis (Batman) – for analyzing Methylated DNA Immunoprecipitation (MeDIP) profiles generated using arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). The latter is an approach we have developed to elucidate the first high-resolution whole-genome DNA methylation profile (DNA methylome) of any mammalian genome. MeDIP-seq/MeDIP-chip combined with Batman represent robust, quantitative, and cost-effective functional genomic strategies for elucidating the function of DNA methylation. PMID:18612301

  15. Combining data and meta-analysis to build Bayesian networks for clinical decision support.

    Science.gov (United States)

    Yet, Barbaros; Perkins, Zane B; Rasmussen, Todd E; Tai, Nigel R M; Marsh, D William R

    2014-12-01

    Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report 'univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model - ignoring some complexities of the problem - or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. Bayesian reliability analysis for non-periodic inspection with estimation of uncertain parameters; Bayesian shinraisei kaiseki wo tekiyoshita hiteiki kozo kensa ni kansuru kenkyu

    Energy Technology Data Exchange (ETDEWEB)

    Itagaki, H. [Yokohama National University, Yokohama (Japan). Faculty of Engineering; Asada, H.; Ito, S. [National Aerospace Laboratory, Tokyo (Japan); Shinozuka, M.

    1996-12-31

    Risk assessed structural positions in a pressurized fuselage of a transport-type aircraft applied with damage tolerance design are taken up as the subject of discussion. A small number of data obtained from inspections on the positions was used to discuss the Bayesian reliability analysis that can estimate also a proper non-periodic inspection schedule, while estimating proper values for uncertain factors. As a result, time period of generating fatigue cracks was determined according to procedure of detailed visual inspections. The analysis method was found capable of estimating values that are thought reasonable and the proper inspection schedule using these values, in spite of placing the fatigue crack progress expression in a very simple form and estimating both factors as the uncertain factors. Thus, the present analysis method was verified of its effectiveness. This study has discussed at the same time the structural positions, modeling of fatigue cracks generated and develop in the positions, conditions for destruction, damage factors, and capability of the inspection from different viewpoints. This reliability analysis method is thought effective also on such other structures as offshore structures. 18 refs., 8 figs., 1 tab.

  17. Bayesian Analysis Made Simple An Excel GUI for WinBUGS

    CERN Document Server

    Woodward, Philip

    2011-01-01

    From simple NLMs to complex GLMMs, this book describes how to use the GUI for WinBUGS - BugsXLA - an Excel add-in written by the author that allows a range of Bayesian models to be easily specified. With case studies throughout, the text shows how to routinely apply even the more complex aspects of model specification, such as GLMMs, outlier robust models, random effects Emax models, auto-regressive errors, and Bayesian variable selection. It provides brief, up-to-date discussions of current issues in the practical application of Bayesian methods. The author also explains how to obtain free so

  18. Statistical analysis of modal parameters of a suspension bridge based on Bayesian spectral density approach and SHM data

    Science.gov (United States)

    Li, Zhijun; Feng, Maria Q.; Luo, Longxi; Feng, Dongming; Xu, Xiuli

    2018-01-01

    Uncertainty of modal parameters estimation appear in structural health monitoring (SHM) practice of civil engineering to quite some significant extent due to environmental influences and modeling errors. Reasonable methodologies are needed for processing the uncertainty. Bayesian inference can provide a promising and feasible identification solution for the purpose of SHM. However, there are relatively few researches on the application of Bayesian spectral method in the modal identification using SHM data sets. To extract modal parameters from large data sets collected by SHM system, the Bayesian spectral density algorithm was applied to address the uncertainty of mode extraction from output-only response of a long-span suspension bridge. The posterior most possible values of modal parameters and their uncertainties were estimated through Bayesian inference. A long-term variation and statistical analysis was performed using the sensor data sets collected from the SHM system of the suspension bridge over a one-year period. The t location-scale distribution was shown to be a better candidate function for frequencies of lower modes. On the other hand, the burr distribution provided the best fitting to the higher modes which are sensitive to the temperature. In addition, wind-induced variation of modal parameters was also investigated. It was observed that both the damping ratios and modal forces increased during the period of typhoon excitations. Meanwhile, the modal damping ratios exhibit significant correlation with the spectral intensities of the corresponding modal forces.

  19. Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms.

    Science.gov (United States)

    Khan, Naveed; McClean, Sally; Zhang, Shuai; Nugent, Chris

    2016-10-26

    In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient's vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%.

  20. Changepoint detection in base-resolution methylome data reveals a robust signature of methylated domain landscape.

    Science.gov (United States)

    Yokoyama, Takao; Miura, Fumihito; Araki, Hiromitsu; Okamura, Kohji; Ito, Takashi

    2015-08-12

    Base-resolution methylome data generated by whole-genome bisulfite sequencing (WGBS) is often used to segment the genome into domains with distinct methylation levels. However, most segmentation methods include many parameters to be carefully tuned and/or fail to exploit the unsurpassed resolution of the data. Furthermore, there is no simple method that displays the composition of the domains to grasp global trends in each methylome. We propose to use changepoint detection for domain demarcation based on base-resolution methylome data. While the proposed method segments the methylome in a largely comparable manner to conventional approaches, it has only a single parameter to be tuned. Furthermore, it fully exploits the base-resolution of the data to enable simultaneous detection of methylation changes in even contrasting size ranges, such as focal hypermethylation and global hypomethylation in cancer methylomes. We also propose a simple plot termed methylated domain landscape (MDL) that globally displays the size, the methylation level and the number of the domains thus defined, thereby enabling one to intuitively grasp trends in each methylome. Since the pattern of MDL often reflects cell lineages and is largely unaffected by data size, it can serve as a novel signature of methylome. Changepoint detection in base-resolution methylome data followed by MDL plotting provides a novel method for methylome characterization and will facilitate global comparison among various WGBS data differing in size and even species origin.

  1. Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models

    Directory of Open Access Journals (Sweden)

    Juhwan Kim

    2018-01-01

    Full Text Available Recent developments in artificial intelligence (AI have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology.

  2. Built environment and Property Crime in Seattle, 1998–2000: A Bayesian Analysis

    Science.gov (United States)

    Matthews, Stephen A.; Yang, Tse-chuan; Hayslett-McCall, Karen L.; Ruback, R. Barry

    2014-01-01

    The past decade has seen a rapid growth in the use of a spatial perspective in studies of crime. In part this growth has been driven by the availability of georeferenced data, and the tools to analyze and visualize them: geographic information systems (GIS), spatial analysis, and spatial statistics. In this paper we use exploratory spatial data analysis (ESDA) tools and Bayesian models to help better understand the spatial patterning and predictors of property crime in Seattle, Washington for 1998–2000, including a focus on built environment variables. We present results for aggregate property crime data as well as models for specific property crime types: residential burglary, nonresidential burglary, theft, auto theft, and arson. ESDA confirms the presence of spatial clustering of property crime and we seek to explain these patterns using spatial Poisson models implemented in WinBUGS. Our results indicate that built environment variables were significant predictors of property crime, especially the presence of a highway on auto theft and burglary. PMID:24737924

  3. Built environment and Property Crime in Seattle, 1998-2000: A Bayesian Analysis.

    Science.gov (United States)

    Matthews, Stephen A; Yang, Tse-Chuan; Hayslett-McCall, Karen L; Ruback, R Barry

    2010-06-01

    The past decade has seen a rapid growth in the use of a spatial perspective in studies of crime. In part this growth has been driven by the availability of georeferenced data, and the tools to analyze and visualize them: geographic information systems (GIS), spatial analysis, and spatial statistics. In this paper we use exploratory spatial data analysis (ESDA) tools and Bayesian models to help better understand the spatial patterning and predictors of property crime in Seattle, Washington for 1998-2000, including a focus on built environment variables. We present results for aggregate property crime data as well as models for specific property crime types: residential burglary, nonresidential burglary, theft, auto theft, and arson. ESDA confirms the presence of spatial clustering of property crime and we seek to explain these patterns using spatial Poisson models implemented in WinBUGS. Our results indicate that built environment variables were significant predictors of property crime, especially the presence of a highway on auto theft and burglary.

  4. Bayesian Analysis for Dynamic Generalized Linear Latent Model with Application to Tree Survival Rate

    Directory of Open Access Journals (Sweden)

    Yu-sheng Cheng

    2014-01-01

    Full Text Available Logistic regression model is the most popular regression technique, available for modeling categorical data especially for dichotomous variables. Classic logistic regression model is typically used to interpret relationship between response variables and explanatory variables. However, in real applications, most data sets are collected in follow-up, which leads to the temporal correlation among the data. In order to characterize the different variables correlations, a new method about the latent variables is introduced in this study. At the same time, the latent variables about AR (1 model are used to depict time dependence. In the framework of Bayesian analysis, parameters estimates and statistical inferences are carried out via Gibbs sampler with Metropolis-Hastings (MH algorithm. Model comparison, based on the Bayes factor, and forecasting/smoothing of the survival rate of the tree are established. A simulation study is conducted to assess the performance of the proposed method and a pika data set is analyzed to illustrate the real application. Since Bayes factor approaches vary significantly, efficiency tests have been performed in order to decide which solution provides a better tool for the analysis of real relational data sets.

  5. Single molecule force spectroscopy at high data acquisition: A Bayesian nonparametric analysis

    Science.gov (United States)

    Sgouralis, Ioannis; Whitmore, Miles; Lapidus, Lisa; Comstock, Matthew J.; Pressé, Steve

    2018-03-01

    Bayesian nonparametrics (BNPs) are poised to have a deep impact in the analysis of single molecule data as they provide posterior probabilities over entire models consistent with the supplied data, not just model parameters of one preferred model. Thus they provide an elegant and rigorous solution to the difficult problem encountered when selecting an appropriate candidate model. Nevertheless, BNPs' flexibility to learn models and their associated parameters from experimental data is a double-edged sword. Most importantly, BNPs are prone to increasing the complexity of the estimated models due to artifactual features present in time traces. Thus, because of experimental challenges unique to single molecule methods, naive application of available BNP tools is not possible. Here we consider traces with time correlations and, as a specific example, we deal with force spectroscopy traces collected at high acquisition rates. While high acquisition rates are required in order to capture dwells in short-lived molecular states, in this setup, a slow response of the optical trap instrumentation (i.e., trapped beads, ambient fluid, and tethering handles) distorts the molecular signals introducing time correlations into the data that may be misinterpreted as true states by naive BNPs. Our adaptation of BNP tools explicitly takes into consideration these response dynamics, in addition to drift and noise, and makes unsupervised time series analysis of correlated single molecule force spectroscopy measurements possible, even at acquisition rates similar to or below the trap's response times.

  6. BAYESIAN WAVELET-BASED CURVE CLASSIFICATION VIA DISCRIMINANT ANALYSIS WITH MARKOV RANDOM TREE PRIORS

    Science.gov (United States)

    Stingo, Francesco C.; Vannucci, Marina; Downey, Gerard

    2014-01-01

    Discriminant analysis is an effective tool for the classification of experimental units into groups. When the number of variables is much larger than the number of observations it is necessary to include a dimension reduction procedure into the inferential process. Here we present a typical example from chemometrics that deals with the classification of different types of food into species via near infrared spectroscopy. We take a nonparametric approach by modeling the functional predictors via wavelet transforms and then apply discriminant analysis in the wavelet domain. We consider a Bayesian conjugate normal discriminant model, either linear or quadratic, that avoids independence assumptions among the wavelet coefficients. We introduce latent binary indicators for the selection of the discriminatory wavelet coefficients and propose prior formulations that use Markov random tree (MRT) priors to map scale-location connections among wavelets coefficients. We conduct posterior inference via MCMC methods, we show performances on our case study on food authenticity and compare results to several other procedures.. PMID:24761126

  7. Estimation of a quantity of interest in uncertainty analysis: Some help from Bayesian decision theory

    International Nuclear Information System (INIS)

    Pasanisi, Alberto; Keller, Merlin; Parent, Eric

    2012-01-01

    In the context of risk analysis under uncertainty, we focus here on the problem of estimating a so-called quantity of interest of an uncertainty analysis problem, i.e. a given feature of the probability distribution function (pdf) of the output of a deterministic model with uncertain inputs. We will stay here in a fully probabilistic setting. A common problem is how to account for epistemic uncertainty tainting the parameter of the probability distribution of the inputs. In the standard practice, this uncertainty is often neglected (plug-in approach). When a specific uncertainty assessment is made, under the basis of the available information (expertise and/or data), a common solution consists in marginalizing the joint distribution of both observable inputs and parameters of the probabilistic model (i.e. computing the predictive pdf of the inputs), then propagating it through the deterministic model. We will reinterpret this approach in the light of Bayesian decision theory, and will put into evidence that this practice leads the analyst to adopt implicitly a specific loss function which may be inappropriate for the problem under investigation, and suboptimal from a decisional perspective. These concepts are illustrated on a simple numerical example, concerning a case of flood risk assessment.

  8. Bayesian Analysis of Evolutionary Divergence with Genomic Data under Diverse Demographic Models.

    Science.gov (United States)

    Chung, Yujin; Hey, Jody

    2017-06-01

    We present a new Bayesian method for estimating demographic and phylogenetic history using population genomic data. Several key innovations are introduced that allow the study of diverse models within an Isolation-with-Migration framework. The new method implements a 2-step analysis, with an initial Markov chain Monte Carlo (MCMC) phase that samples simple coalescent trees, followed by the calculation of the joint posterior density for the parameters of a demographic model. In step 1, the MCMC sampling phase, the method uses a reduced state space, consisting of coalescent trees without migration paths, and a simple importance sampling distribution without the demography of interest. Once obtained, a single sample of trees can be used in step 2 to calculate the joint posterior density for model parameters under multiple diverse demographic models, without having to repeat MCMC runs. Because migration paths are not included in the state space of the MCMC phase, but rather are handled by analytic integration in step 2 of the analysis, the method is scalable to a large number of loci with excellent MCMC mixing properties. With an implementation of the new method in the computer program MIST, we demonstrate the method's accuracy, scalability, and other advantages using simulated data and DNA sequences of two common chimpanzee subspecies: Pan troglodytes (P. t.) troglodytes and P. t. verus. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  9. Bayesian Analysis Diagnostics: Diagnosing Predictive and Parameter Uncertainty for Hydrological Models

    Science.gov (United States)

    Thyer, Mark; Kavetski, Dmitri; Evin, Guillaume; Kuczera, George; Renard, Ben; McInerney, David

    2015-04-01

    All scientific and statistical analysis, particularly in natural sciences, is based on approximations and assumptions. For example, the calibration of hydrological models using approaches such as Nash-Sutcliffe efficiency and/or simple least squares (SLS) objective functions may appear to be 'assumption-free'. However, this is a naïve point of view, as SLS assumes that the model residuals (residuals=observed-predictions) are independent, homoscedastic and Gaussian. If these assumptions are poor, parameter inference and model predictions will be correspondingly poor. An essential step in model development is therefore to verify the assumptions and approximations made in the modeling process. Diagnostics play a key role in verifying modeling assumptions. An important advantage of the formal Bayesian approach is that the modeler is required to make the assumptions explicit. Specialized diagnostics can then be developed and applied to test and verify their assumptions. This paper presents a suite of statistical and modeling diagnostics that can be used by environmental modelers to test their modeling calibration assumptions and diagnose model deficiencies. Three major types of diagnostics are presented: Residual Diagnostics Residual diagnostics are used to test whether the assumptions of the residual error model within the likelihood function are compatible with the data. This includes testing for statistical independence, homoscedasticity, unbiasedness, Gaussianity and any distributional assumptions. Parameter Uncertainty and MCMC Diagnostics An important part of Bayesian analysis is assess parameter uncertainty. Markov Chain Monte Carlo (MCMC) methods are a powerful numerical tool for estimating these uncertainties. Diagnostics based on posterior parameter distributions can be used to assess parameter identifiability, interactions and correlations. This provides a very useful tool for detecting and remedying model deficiencies. In addition, numerical diagnostics are

  10. Wavelet-Based Bayesian Methods for Image Analysis and Automatic Target Recognition

    National Research Council Canada - National Science Library

    Nowak, Robert

    2001-01-01

    .... We have developed two new techniques. First, we have develop a wavelet-based approach to image restoration and deconvolution problems using Bayesian image models and an alternating-maximation method...

  11. Bus Route Design with a Bayesian Network Analysis of Bus Service Revenues

    OpenAIRE

    Liu, Yi; Jia, Yuanhua; Feng, Xuesong; Wu, Jiang

    2018-01-01

    A Bayesian network is used to estimate revenues of bus services in consideration of the effect of bus travel demands, passenger transport distances, and so on. In this research, the area X in Beijing has been selected as the study area because of its relatively high bus travel demand and, on the contrary, unsatisfactory bus services. It is suggested that the proposed Bayesian network approach is able to rationally predict the probabilities of different revenues of various route services, from...

  12. A Bayesian network meta-analysis on second-line systemic therapy in advanced gastric cancer.

    Science.gov (United States)

    Zhu, Xiaofu; Ko, Yoo-Joung; Berry, Scott; Shah, Keya; Lee, Esther; Chan, Kelvin

    2017-07-01

    It is unclear which regimen is the most efficacious among the available therapies for advanced gastric cancer in the second-line setting. We performed a network meta-analysis to determine their relative benefits. We conducted a systematic review of randomized controlled trials (RCTs) through the MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases and American Society of Clinical Oncology abstracts up to June 2014 to identify phase III RCTs on advanced gastric cancer in the second-line setting. Overall survival (OS) data were the primary outcome of interest. Hazard ratios (HRs) were extracted from the publications on the basis of reported values or were extracted from survival curves by established methods. A Bayesian network meta-analysis was performed with WinBUGS to compare all regimens simultaneously. Eight RCTs (2439 patients) were identified and contained extractable data for quantitative analysis. Network meta-analysis showed that paclitaxel plus ramucirumab was superior to single-agent ramucirumab [OS HR 0.51, 95 % credible region (CR) 0.30-0.86], paclitaxel (OS HR 0.81, 95 % CR 0.68-0.96), docetaxel (OS HR 0.56, 95 % CR 0.33-0.94), and irinotecan (OS HR 0.71, 95 % CR 0.52-0.99). Paclitaxel plus ramucirumab also had an 89 % probability of being the best regimen among all these regimens. Single-agent ramucirumab, paclitaxel, docetaxel, and irinotecan were comparable to each other with respect to OS and were superior to best supportive care. This is the first network meta-analysis to compare all second-line regimens reported in phase III gastric cancer trials. The results suggest the paclitaxel plus ramucirumab combination is the most effective therapy and should be the reference regimen for future comparative trials.

  13. A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding.

    Science.gov (United States)

    McCandless, Lawrence C; Gustafson, Paul

    2017-08-15

    Bias from unmeasured confounding is a persistent concern in observational studies, and sensitivity analysis has been proposed as a solution. In the recent years, probabilistic sensitivity analysis using either Monte Carlo sensitivity analysis (MCSA) or Bayesian sensitivity analysis (BSA) has emerged as a practical analytic strategy when there are multiple bias parameters inputs. BSA uses Bayes theorem to formally combine evidence from the prior distribution and the data. In contrast, MCSA samples bias parameters directly from the prior distribution. Intuitively, one would think that BSA and MCSA ought to give similar results. Both methods use similar models and the same (prior) probability distributions for the bias parameters. In this paper, we illustrate the surprising finding that BSA and MCSA can give very different results. Specifically, we demonstrate that MCSA can give inaccurate uncertainty assessments (e.g. 95% intervals) that do not reflect the data's influence on uncertainty about unmeasured confounding. Using a data example from epidemiology and simulation studies, we show that certain combinations of data and prior distributions can result in dramatic prior-to-posterior changes in uncertainty about the bias parameters. This occurs because the application of Bayes theorem in a non-identifiable model can sometimes rule out certain patterns of unmeasured confounding that are not compatible with the data. Consequently, the MCSA approach may give 95% intervals that are either too wide or too narrow and that do not have 95% frequentist coverage probability. Based on our findings, we recommend that analysts use BSA for probabilistic sensitivity analysis. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  14. BAYESIAN ANALYSIS TO IDENTIFY NEW STAR CANDIDATES IN NEARBY YOUNG STELLAR KINEMATIC GROUPS

    International Nuclear Information System (INIS)

    Malo, Lison; Doyon, René; Lafrenière, David; Artigau, Étienne; Gagné, Jonathan; Baron, Frédérique; Riedel, Adric

    2013-01-01

    We present a new method based on a Bayesian analysis to identify new members of nearby young kinematic groups. The analysis minimally takes into account the position, proper motion, magnitude, and color of a star, but other observables can be readily added (e.g., radial velocity, distance). We use this method to find new young low-mass stars in the β Pictoris and AB Doradus moving groups and in the TW Hydrae, Tucana-Horologium, Columba, Carina, and Argus associations. Starting from a sample of 758 mid-K to mid-M (K5V-M5V) stars showing youth indicators such as Hα and X-ray emission, our analysis yields 214 new highly probable low-mass members of the kinematic groups analyzed. One is in TW Hydrae, 37 in β Pictoris, 17 in Tucana-Horologium, 20 in Columba, 6 in Carina, 50 in Argus, 32 in AB Doradus, and the remaining 51 candidates are likely young but have an ambiguous membership to more than one association. The false alarm rate for new candidates is estimated to be 5% for β Pictoris and TW Hydrae, 10% for Tucana-Horologium, Columba, Carina, and Argus, and 14% for AB Doradus. Our analysis confirms the membership of 58 stars proposed in the literature. Firm membership confirmation of our new candidates will require measurement of their radial velocity (predicted by our analysis), parallax, and lithium 6708 Å equivalent width. We have initiated these follow-up observations for a number of candidates, and we have identified two stars (2MASSJ01112542+1526214, 2MASSJ05241914-1601153) as very strong candidate members of the β Pictoris moving group and one strong candidate member (2MASSJ05332558-5117131) of the Tucana-Horologium association; these three stars have radial velocity measurements confirming their membership and lithium detections consistent with young age.

  15. Cyclist activity and injury risk analysis at signalized intersections: a Bayesian modelling approach.

    Science.gov (United States)

    Strauss, Jillian; Miranda-Moreno, Luis F; Morency, Patrick

    2013-10-01

    This study proposes a two-equation Bayesian modelling approach to simultaneously study cyclist injury occurrence and bicycle activity at signalized intersections as joint outcomes. This approach deals with the potential presence of endogeneity and unobserved heterogeneities and is used to identify factors associated with both cyclist injuries and volumes. Its application to identify high-risk corridors is also illustrated. Montreal, Quebec, Canada is the application environment, using an extensive inventory of a large sample of signalized intersections containing disaggregate motor-vehicle traffic volumes and bicycle flows, geometric design, traffic control and built environment characteristics in the vicinity of the intersections. Cyclist injury data for the period of 2003-2008 is used in this study. Also, manual bicycle counts were standardized using temporal and weather adjustment factors to obtain average annual daily volumes. Results confirm and quantify the effects of both bicycle and motor-vehicle flows on cyclist injury occurrence. Accordingly, more cyclists at an intersection translate into more cyclist injuries but lower injury rates due to the non-linear association between bicycle volume and injury occurrence. Furthermore, the results emphasize the importance of turning motor-vehicle movements. The presence of bus stops and total crosswalk length increase cyclist injury occurrence whereas the presence of a raised median has the opposite effect. Bicycle activity through intersections was found to increase as employment, number of metro stations, land use mix, area of commercial land use type, length of bicycle facilities and the presence of schools within 50-800 m of the intersection increase. Intersections with three approaches are expected to have fewer cyclists than those with four. Using Bayesian analysis, expected injury frequency and injury rates were estimated for each intersection and used to rank corridors. Corridors with high bicycle volumes

  16. No control genes required: Bayesian analysis of qRT-PCR data.

    Directory of Open Access Journals (Sweden)

    Mikhail V Matz

    Full Text Available Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process.In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts. Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the "classic" analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests.Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.

  17. No control genes required: Bayesian analysis of qRT-PCR data.

    Science.gov (United States)

    Matz, Mikhail V; Wright, Rachel M; Scott, James G

    2013-01-01

    Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process. In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the "classic" analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests. Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.

  18. A Hybrid Approach for Reliability Analysis Based on Analytic Hierarchy Process and Bayesian Network

    International Nuclear Information System (INIS)

    Zubair, Muhammad

    2014-01-01

    By using analytic hierarchy process (AHP) and Bayesian Network (BN) the present research signifies the technical and non-technical issues of nuclear accidents. The study exposed that the technical faults was one major reason of these accidents. Keep an eye on other point of view it becomes clearer that human behavior like dishonesty, insufficient training, and selfishness are also play a key role to cause these accidents. In this study, a hybrid approach for reliability analysis based on AHP and BN to increase nuclear power plant (NPP) safety has been developed. By using AHP, best alternative to improve safety, design, operation, and to allocate budget for all technical and non-technical factors related with nuclear safety has been investigated. We use a special structure of BN based on the method AHP. The graphs of the BN and the probabilities associated with nodes are designed to translate the knowledge of experts on the selection of best alternative. The results show that the improvement in regulatory authorities will decrease failure probabilities and increase safety and reliability in industrial area.

  19. Nonparametric Bayesian inference for mean residual life functions in survival analysis.

    Science.gov (United States)

    Poynor, Valerie; Kottas, Athanasios

    2018-01-19

    Modeling and inference for survival analysis problems typically revolves around different functions related to the survival distribution. Here, we focus on the mean residual life (MRL) function, which provides the expected remaining lifetime given that a subject has survived (i.e. is event-free) up to a particular time. This function is of direct interest in reliability, medical, and actuarial fields. In addition to its practical interpretation, the MRL function characterizes the survival distribution. We develop general Bayesian nonparametric inference for MRL functions built from a Dirichlet process mixture model for the associated survival distribution. The resulting model for the MRL function admits a representation as a mixture of the kernel MRL functions with time-dependent mixture weights. This model structure allows for a wide range of shapes for the MRL function. Particular emphasis is placed on the selection of the mixture kernel, taken to be a gamma distribution, to obtain desirable properties for the MRL function arising from the mixture model. The inference method is illustrated with a data set of two experimental groups and a data set involving right censoring. The supplementary material available at Biostatistics online provides further results on empirical performance of the model, using simulated data examples. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  20. Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements

    KAUST Repository

    Ryu, Duchwan

    2010-09-28

    We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.

  1. Bayesian linear regression with skew-symmetric error distributions with applications to survival analysis

    KAUST Repository

    Rubio, Francisco J.

    2016-02-09

    We study Bayesian linear regression models with skew-symmetric scale mixtures of normal error distributions. These kinds of models can be used to capture departures from the usual assumption of normality of the errors in terms of heavy tails and asymmetry. We propose a general noninformative prior structure for these regression models and show that the corresponding posterior distribution is proper under mild conditions. We extend these propriety results to cases where the response variables are censored. The latter scenario is of interest in the context of accelerated failure time models, which are relevant in survival analysis. We present a simulation study that demonstrates good frequentist properties of the posterior credible intervals associated with the proposed priors. This study also sheds some light on the trade-off between increased model flexibility and the risk of over-fitting. We illustrate the performance of the proposed models with real data. Although we focus on models with univariate response variables, we also present some extensions to the multivariate case in the Supporting Information.

  2. Identification of Watershed-scale Critical Source Areas Using Bayesian Maximum Entropy Spatiotemporal Analysis

    Science.gov (United States)

    Roostaee, M.; Deng, Z.

    2017-12-01

    The states' environmental agencies are required by The Clean Water Act to assess all waterbodies and evaluate potential sources of impairments. Spatial and temporal distributions of water quality parameters are critical in identifying Critical Source Areas (CSAs). However, due to limitations in monetary resources and a large number of waterbodies, available monitoring stations are typically sparse with intermittent periods of data collection. Hence, scarcity of water quality data is a major obstacle in addressing sources of pollution through management strategies. In this study spatiotemporal Bayesian Maximum Entropy method (BME) is employed to model the inherent temporal and spatial variability of measured water quality indicators such as Dissolved Oxygen (DO) concentration for Turkey Creek Watershed. Turkey Creek is located in northern Louisiana and has been listed in 303(d) list for DO impairment since 2014 in Louisiana Water Quality Inventory Reports due to agricultural practices. BME method is proved to provide more accurate estimates than the methods of purely spatial analysis by incorporating space/time distribution and uncertainty in available measured soft and hard data. This model would be used to estimate DO concentration at unmonitored locations and times and subsequently identifying CSAs. The USDA's crop-specific land cover data layers of the watershed were then used to determine those practices/changes that led to low DO concentration in identified CSAs. Primary results revealed that cultivation of corn and soybean as well as urban runoff are main contributing sources in low dissolved oxygen in Turkey Creek Watershed.

  3. Assessment of occupational safety risks in Floridian solid waste systems using Bayesian analysis.

    Science.gov (United States)

    Bastani, Mehrad; Celik, Nurcin

    2015-10-01

    Safety risks embedded within solid waste management systems continue to be a significant issue and are prevalent at every step in the solid waste management process. To recognise and address these occupational hazards, it is necessary to discover the potential safety concerns that cause them, as well as their direct and/or indirect impacts on the different types of solid waste workers. In this research, our goal is to statistically assess occupational safety risks to solid waste workers in the state of Florida. Here, we first review the related standard industrial codes to major solid waste management methods including recycling, incineration, landfilling, and composting. Then, a quantitative assessment of major risks is conducted based on the data collected using a Bayesian data analysis and predictive methods. The risks estimated in this study for the period of 2005-2012 are then compared with historical statistics (1993-1997) from previous assessment studies. The results have shown that the injury rates among refuse collectors in both musculoskeletal and dermal injuries have decreased from 88 and 15 to 16 and three injuries per 1000 workers, respectively. However, a contrasting trend is observed for the injury rates among recycling workers, for whom musculoskeletal and dermal injuries have increased from 13 and four injuries to 14 and six injuries per 1000 workers, respectively. Lastly, a linear regression model has been proposed to identify major elements of the high number of musculoskeletal and dermal injuries. © The Author(s) 2015.

  4. Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices.

    Science.gov (United States)

    Runcie, Daniel E; Mukherjee, Sayan

    2013-07-01

    Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.

  5. Integration of Bayesian analysis for eutrophication prediction and assessment in a landscape lake.

    Science.gov (United States)

    Yang, Likun; Zhao, Xinhua; Peng, Sen; Zhou, Guangyu

    2015-01-01

    Eutrophication models have been widely used to assess water quality in landscape lakes. Because flow rate in landscape lakes is relatively low and similar to that of natural lakes, eutrophication is more dominant in landscape lakes. To assess the risk of eutrophication in landscape lakes, a set of dynamic equations was developed to simulate lake water quality for total nitrogen (TN), total phosphorous (TP), dissolve oxygen (DO) and chlorophyll a (Chl a). Firstly, the Bayesian calibration results were described. Moreover, the ability of the model to reproduce adequately the observed mean patterns and major cause-effect relationships for water quality conditions in landscape lakes were presented. Two loading scenarios were used. A Monte Carlo algorithm was applied to calculate the predicated water quality distributions, which were used in the established hierarchical assessment system for lake water quality risk. The important factors affecting the lake water quality risk were defined using linear regression analysis. The results indicated that the variations in the landscape lake receiving recharge water quality caused considerable landscape lake water quality risk in the surrounding area. Moreover, the Chl a concentration in lake water was significantly affected by TP and TN concentrations; the lake TP concentration was the limiting factor for growth of plankton in lake water. The lake water TN concentration provided the basic nutritional requirements. Lastly, lower TN and TP concentrations in the receiving recharge water caused increased lake water quality risk.

  6. Bayesian probability analysis: a prospective demonstration of its clinical utility in diagnosing coronary disease

    International Nuclear Information System (INIS)

    Detrano, R.; Yiannikas, J.; Salcedo, E.E.; Rincon, G.; Go, R.T.; Williams, G.; Leatherman, J.

    1984-01-01

    One hundred fifty-four patients referred for coronary arteriography were prospectively studied with stress electrocardiography, stress thallium scintigraphy, cine fluoroscopy (for coronary calcifications), and coronary angiography. Pretest probabilities of coronary disease were determined based on age, sex, and type of chest pain. These and pooled literature values for the conditional probabilities of test results based on disease state were used in Bayes theorem to calculate posttest probabilities of disease. The results of the three noninvasive tests were compared for statistical independence, a necessary condition for their simultaneous use in Bayes theorem. The test results were found to demonstrate pairwise independence in patients with and those without disease. Some dependencies that were observed between the test results and the clinical variables of age and sex were not sufficient to invalidate application of the theorem. Sixty-eight of the study patients had at least one major coronary artery obstruction of greater than 50%. When these patients were divided into low-, intermediate-, and high-probability subgroups according to their pretest probabilities, noninvasive test results analyzed by Bayesian probability analysis appropriately advanced 17 of them by at least one probability subgroup while only seven were moved backward. Of the 76 patients without disease, 34 were appropriately moved into a lower probability subgroup while 10 were incorrectly moved up. We conclude that posttest probabilities calculated from Bayes theorem more accurately classified patients with and without disease than did pretest probabilities, thus demonstrating the utility of the theorem in this application

  7. Paired Comparison Analysis of the van Baaren Model Using Bayesian Approach with Noninformative Prior

    Directory of Open Access Journals (Sweden)

    Saima Altaf

    2012-03-01

    Full Text Available 800x600 Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} One technique being commonly studied these days because of its attractive applications for the comparison of several objects is the method of paired comparisons. This technique permits the ranking of the objects by means of a score, which reflects the merit of the items on a linear scale. The present study is concerned with the Bayesian analysis of a paired comparison model, namely the van Baaren model VI using noninformative uniform prior. For this purpose, the joint posterior distribution for the parameters of the model, their marginal distributions, posterior estimates (means and modes, the posterior probabilities for comparing the two treatment parameters and the predictive probabilities are obtained.

  8. Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE

    Directory of Open Access Journals (Sweden)

    Brentani Helena

    2004-08-01

    Full Text Available Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE, "Digital Northern" or Massively Parallel Signature Sequencing (MPSS, is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries" and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

  9. Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection.

    Science.gov (United States)

    Dhavala, Soma S; Datta, Sujay; Mallick, Bani K; Carroll, Raymond J; Khare, Sangeeta; Lawhon, Sara D; Adams, L Garry

    2010-09-01

    Massively Parallel Signature Sequencing (MPSS) is a high-throughput counting-based technology available for gene expression profiling. It produces output that is similar to Serial Analysis of Gene Expression (SAGE) and is ideal for building complex relational databases for gene expression. Our goal is to compare the in vivo global gene expression profiles of tissues infected with different strains of Salmonella obtained using the MPSS technology. In this article, we develop an exact ANOVA type model for this count data using a zero-inflated Poisson (ZIP) distribution, different from existing methods that assume continuous densities. We adopt two Bayesian hierarchical models-one parametric and the other semiparametric with a Dirichlet process prior that has the ability to "borrow strength" across related signatures, where a signature is a specific arrangement of the nucleotides, usually 16-21 base-pairs long. We utilize the discreteness of Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using non-parametric approaches, while controlling the false discovery rate. We identify several differentially expressed genes that have important biological significance and conclude with a summary of the biological discoveries.

  10. Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE).

    Science.gov (United States)

    Vêncio, Ricardo Z N; Brentani, Helena; Patrão, Diogo F C; Pereira, Carlos A B

    2004-08-31

    An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

  11. To be certain about the uncertainty: Bayesian statistics for 13 C metabolic flux analysis.

    Science.gov (United States)

    Theorell, Axel; Leweke, Samuel; Wiechert, Wolfgang; Nöh, Katharina

    2017-11-01

    13 C Metabolic Fluxes Analysis ( 13 C MFA) remains to be the most powerful approach to determine intracellular metabolic reaction rates. Decisions on strain engineering and experimentation heavily rely upon the certainty with which these fluxes are estimated. For uncertainty quantification, the vast majority of 13 C MFA studies relies on confidence intervals from the paradigm of Frequentist statistics. However, it is well known that the confidence intervals for a given experimental outcome are not uniquely defined. As a result, confidence intervals produced by different methods can be different, but nevertheless equally valid. This is of high relevance to 13 C MFA, since practitioners regularly use three different approximate approaches for calculating confidence intervals. By means of a computational study with a realistic model of the central carbon metabolism of E. coli, we provide strong evidence that confidence intervals used in the field depend strongly on the technique with which they were calculated and, thus, their use leads to misinterpretation of the flux uncertainty. In order to provide a better alternative to confidence intervals in 13 C MFA, we demonstrate that credible intervals from the paradigm of Bayesian statistics give more reliable flux uncertainty quantifications which can be readily computed with high accuracy using Markov chain Monte Carlo. In addition, the widely applied chi-square test, as a means of testing whether the model reproduces the data, is examined closer. © 2017 Wiley Periodicals, Inc.

  12. A Bayesian Approach to the Design and Analysis of Computer Experiments

    Energy Technology Data Exchange (ETDEWEB)

    Currin, C.

    1988-01-01

    We consider the problem of designing and analyzing experiments for prediction of the function y(f), t {element_of} T, where y is evaluated by means of a computer code (typically by solving complicated equations that model a physical system), and T represents the domain of inputs to the code. We use a Bayesian approach, in which uncertainty about y is represented by a spatial stochastic process (random function); here we restrict attention to stationary Gaussian processes. The posterior mean function can be used as an interpolating function, with uncertainties given by the posterior standard deviations. Instead of completely specifying the prior process, we consider several families of priors, and suggest some cross-validational methods for choosing one that performs relatively well on the function at hand. As a design criterion, we use the expected reduction in the entropy of the random vector y (T*), where T* {contained_in} T is a given finite set of ''sites'' (input configurations) at which predictions are to be made. We describe an exchange algorithm for constructing designs that are optimal with respect to this criterion. To demonstrate the use of these design and analysis methods, several examples are given, including one experiment on a computer model of a thermal energy storage device and another on an integrated circuit simulator.

  13. Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection

    KAUST Repository

    Dhavala, Soma S.

    2010-09-01

    Massively Parallel Signature Sequencing (MPSS) is a high-throughput, counting-based technology available for gene expression profiling. It produces output that is similar to Serial Analysis of Gene Expression and is ideal for building complex relational databases for gene expression. Our goal is to compare the in vivo global gene expression profiles of tissues infected with different strains of Salmonella obtained using the MPSS technology. In this article, we develop an exact ANOVA type model for this count data using a zero-inflatedPoisson distribution, different from existing methods that assume continuous densities. We adopt two Bayesian hierarchical models-one parametric and the other semiparametric with a Dirichlet process prior that has the ability to "borrow strength" across related signatures, where a signature is a specific arrangement of the nucleotides, usually 16-21 base pairs long. We utilize the discreteness of Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using nonparametric approaches, while controlling the false discovery rate. We identify several differentially expressed genes that have important biological significance and conclude with a summary of the biological discoveries. This article has supplementary materials online. © 2010 American Statistical Association.

  14. Composite behavior analysis for video surveillance using hierarchical dynamic Bayesian networks

    Science.gov (United States)

    Cheng, Huanhuan; Shan, Yong; Wang, Runsheng

    2011-03-01

    Analyzing composite behaviors involving objects from multiple categories in surveillance videos is a challenging task due to the complicated relationships among human and objects. This paper presents a novel behavior analysis framework using a hierarchical dynamic Bayesian network (DBN) for video surveillance systems. The model is built for extracting objects' behaviors and their relationships by representing behaviors using spatial-temporal characteristics. The recognition of object behaviors is processed by the DBN at multiple levels: features of objects at low level, objects and their relationships at middle level, and event at high level, where event refers to behaviors of a single type object as well as behaviors consisting of several types of objects such as ``a person getting in a car.'' Furthermore, to reduce the complexity, a simple model selection criterion is addressed, by which the appropriated model is picked out from a pool of candidate models. Experiments are shown to demonstrate that the proposed framework could efficiently recognize and semantically describe composite object and human activities in surveillance videos.

  15. Intrinsic Properties of tRNA Molecules as Deciphered via Bayesian Network and Distribution Divergence Analysis

    Directory of Open Access Journals (Sweden)

    Sergio Branciamore

    2018-02-01

    Full Text Available The identity/recognition of tRNAs, in the context of aminoacyl tRNA synthetases (and other molecules, is a complex phenomenon that has major implications ranging from the origins and evolution of translation machinery and genetic code to the evolution and speciation of tRNAs themselves to human mitochondrial diseases to artificial genetic code engineering. Deciphering it via laboratory experiments, however, is difficult and necessarily time- and resource-consuming. In this study, we propose a mathematically rigorous two-pronged in silico approach to identifying and classifying tRNA positions important for tRNA identity/recognition, rooted in machine learning and information-theoretic methodology. We apply Bayesian Network modeling to elucidate the structure of intra-tRNA-molecule relationships, and distribution divergence analysis to identify meaningful inter-molecule differences between various tRNA subclasses. We illustrate the complementary application of these two approaches using tRNA examples across the three domains of life, and identify and discuss important (informative positions therein. In summary, we deliver to the tRNA research community a novel, comprehensive methodology for identifying the specific elements of interest in various tRNA molecules, which can be followed up by the corresponding experimental work and/or high-resolution position-specific statistical analyses.

  16. Associations between sexual habits, menstrual hygiene practices, demographics and the vaginal microbiome as revealed by Bayesian network analysis

    OpenAIRE

    Noyes, Noelle; Cho, Kyu-Chul; Ravel, Jacques; Forney, Larry J.; Abdo, Zaid

    2018-01-01

    The vaginal microbiome plays an influential role in several disease states in reproductive age women, including bacterial vaginosis (BV). While demographic characteristics are associated with differences in vaginal microbiome community structure, little is known about the influence of sexual and hygiene habits. Furthermore, associations between the vaginal microbiome and risk symptoms of bacterial vaginosis have not been fully elucidated. Using Bayesian network (BN) analysis of 16S rRNA gene ...

  17. Bayesian Analysis for Food-Safety Risk Assessment: Evaluation of Dose-Response Functions within WinBUGS

    OpenAIRE

    Williams, Michael S.; Ebel, Eric D.; Hoeting, Jennifer A.

    2011-01-01

    Bayesian methods are becoming increasingly popular in the field of food-safety risk assessment. Risk assessment models often require the integration of a dose-response function over the distribution of all possible doses of a pathogen ingested with a specific food. This requires the evaluation of an integral for every sample for a Markov chain Monte Carlo analysis of a model. While many statistical software packages have functions that allow for the evaluation of the integral, this functional...

  18. The phylogeographic history of the new world screwworm fly, inferred by approximate bayesian computation analysis.

    Directory of Open Access Journals (Sweden)

    Pablo Fresia

    Full Text Available Insect pest phylogeography might be shaped both by biogeographic events and by human influence. Here, we conducted an approximate Bayesian computation (ABC analysis to investigate the phylogeography of the New World screwworm fly, Cochliomyia hominivorax, with the aim of understanding its population history and its order and time of divergence. Our ABC analysis supports that populations spread from North to South in the Americas, in at least two different moments. The first split occurred between the North/Central American and South American populations in the end of the Last Glacial Maximum (15,300-19,000 YBP. The second split occurred between the North and South Amazonian populations in the transition between the Pleistocene and the Holocene eras (9,100-11,000 YBP. The species also experienced population expansion. Phylogenetic analysis likewise suggests this north to south colonization and Maxent models suggest an increase in the number of suitable areas in South America from the past to present. We found that the phylogeographic patterns observed in C. hominivorax cannot be explained only by climatic oscillations and can be connected to host population histories. Interestingly we found these patterns are very coincident with general patterns of ancient human movements in the Americas, suggesting that humans might have played a crucial role in shaping the distribution and population structure of this insect pest. This work presents the first hypothesis test regarding the processes that shaped the current phylogeographic structure of C. hominivorax and represents an alternate perspective on investigating the problem of insect pests.

  19. Bayesian biostatistics

    CERN Document Server

    Lesaffre, Emmanuel

    2012-01-01

    The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd

  20. Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

    Directory of Open Access Journals (Sweden)

    Hero Alfred

    2010-11-01

    Full Text Available Abstract Background Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP, the Indian Buffet Process (IBP, and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB analysis. Results Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV, Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD, closely related non-Bayesian approaches. Conclusions Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

  1. Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness.

    Science.gov (United States)

    Tang, Niansheng; Chow, Sy-Miin; Ibrahim, Joseph G; Zhu, Hongtu

    2017-12-01

    Many psychological concepts are unobserved and usually represented as latent factors apprehended through multiple observed indicators. When multiple-subject multivariate time series data are available, dynamic factor analysis models with random effects offer one way of modeling patterns of within- and between-person variations by combining factor analysis and time series analysis at the factor level. Using the Dirichlet process (DP) as a nonparametric prior for individual-specific time series parameters further allows the distributional forms of these parameters to deviate from commonly imposed (e.g., normal or other symmetric) functional forms, arising as a result of these parameters' restricted ranges. Given the complexity of such models, a thorough sensitivity analysis is critical but computationally prohibitive. We propose a Bayesian local influence method that allows for simultaneous sensitivity analysis of multiple modeling components within a single fitting of the model of choice. Five illustrations and an empirical example are provided to demonstrate the utility of the proposed approach in facilitating the detection of outlying cases and common sources of misspecification in dynamic factor analysis models, as well as identification of modeling components that are sensitive to changes in the DP prior specification.

  2. A Bayesian SIRS model for the analysis of respiratory syncytial virus in the region of Valencia, Spain.

    Science.gov (United States)

    Corberán-Vallet, Ana; Santonja, Francisco J

    2014-09-01

    We present a Bayesian stochastic susceptible-infected-recovered-susceptible (SIRS) model in discrete time to understand respiratory syncytial virus dynamics in the region of Valencia, Spain. A SIRS model based on ordinary differential equations has also been proposed to describe RSV dynamics in the region of Valencia. However, this continuous-time deterministic model is not suitable when the initial number of infected individuals is small. Stochastic epidemic models based on a probability of disease transmission provide a more natural description of the spread of infectious diseases. In addition, by allowing the transmission rate to vary stochastically over time, the proposed model provides an improved description of RSV dynamics. The Bayesian analysis of the model allows us to calculate both the posterior distribution of the model parameters and the posterior predictive distribution, which facilitates the computation of point forecasts and prediction intervals for future observations. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Reconstruction of a beech population bottleneck using archival demographic information and Bayesian analysis of genetic data.

    Science.gov (United States)

    Lander, Tonya A; Oddou-Muratorio, Sylvie; Prouillet-Leplat, Helene; Klein, Etienne K

    2011-12-01

    Range expansion and contraction has occurred in the history of most species and can seriously impact patterns of genetic diversity. Historical data about range change are rare and generally appropriate for studies at large scales, whereas the individual pollen and seed dispersal events that form the basis of geneflow and colonization generally occur at a local scale. In this study, we investigated range change in Fagus sylvatica on Mont Ventoux, France, using historical data from 1838 to the present and approximate Bayesian computation (ABC) analyses of genetic data. From the historical data, we identified a population minimum in 1845 and located remnant populations at least 200 years old. The ABC analysis selected a demographic scenario with three populations, corresponding to two remnant populations and one area of recent expansion. It also identified expansion from a smaller ancestral population but did not find that this expansion followed a population bottleneck, as suggested by the historical data. Despite a strong support to the selected scenario for our data set, the ABC approach showed a low power to discriminate among scenarios on average and a low ability to accurately estimate effective population sizes and divergence dates, probably due to the temporal scale of the study. This study provides an unusual opportunity to test ABC analysis in a system with a well-documented demographic history and identify discrepancies between the results of historical, classical population genetic and ABC analyses. The results also provide valuable insights into genetic processes at work at a fine spatial and temporal scale in range change and colonization. © 2011 Blackwell Publishing Ltd.

  4. Bayesian network modeling: A case study of an epidemiologic system analysis of cardiovascular risk.

    Science.gov (United States)

    Fuster-Parra, P; Tauler, P; Bennasar-Veny, M; Ligęza, A; López-González, A A; Aguiló, A

    2016-04-01

    An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  5. Bayesian analysis of the dynamic cosmic web in the SDSS galaxy survey

    International Nuclear Information System (INIS)

    Leclercq, Florent; Wandelt, Benjamin; Jasche, Jens

    2015-01-01

    Recent application of the Bayesian algorithm \\textsc(borg) to the Sloan Digital Sky Survey (SDSS) main sample galaxies resulted in the physical inference of the formation history of the observed large-scale structure from its origin to the present epoch. In this work, we use these inferences as inputs for a detailed probabilistic cosmic web-type analysis. To do so, we generate a large set of data-constrained realizations of the large-scale structure using a fast, fully non-linear gravitational model. We then perform a dynamic classification of the cosmic web into four distinct components (voids, sheets, filaments, and clusters) on the basis of the tidal field. Our inference framework automatically and self-consistently propagates typical observational uncertainties to web-type classification. As a result, this study produces accurate cosmographic classification of large-scale structure elements in the SDSS volume. By also providing the history of these structure maps, the approach allows an analysis of the origin and growth of the early traces of the cosmic web present in the initial density field and of the evolution of global quantities such as the volume and mass filling fractions of different structures. For the problem of web-type classification, the results described in this work constitute the first connection between theory and observations at non-linear scales including a physical model of structure formation and the demonstrated capability of uncertainty quantification. A connection between cosmology and information theory using real data also naturally emerges from our probabilistic approach. Our results constitute quantitative chrono-cosmography of the complex web-like patterns underlying the observed galaxy distribution

  6. How few countries will do? Comparative survey analysis from a Bayesian perspective

    Directory of Open Access Journals (Sweden)

    Joop J.C.M. Hox

    2012-07-01

    Full Text Available Meuleman and Billiet (2009 have carried out a simulation study aimed at the question how many countries are needed for accurate multilevel SEM estimation in comparative studies. The authors concluded that a sample of 50 to 100 countries is needed for accurate estimation. Recently, Bayesian estimation methods have been introduced in structural equation modeling which should work well with much lower sample sizes. The current study reanalyzes the simulation of Meuleman and Billiet using Bayesian estimation to find the lowest number of countries needed when conducting multilevel SEM. The main result of our simulations is that a sample of about 20 countries is sufficient for accurate Bayesian estimation, which makes multilevel SEM practicable for the number of countries commonly available in large scale comparative surveys.

  7. Bayesian ideas and data analysis an introduction for scientists and statisticians

    CERN Document Server

    Christensen, Ronald; Branscum, Adam; Hanson, Timothy E.

    2010-01-01

    This book provides a good introduction to Bayesian approaches to applied statistical modelling. … The authors have fulfilled their main aim of introducing Bayesian ideas through examples using a large number of statistical models. An interesting feature of this book is the humour of the authors that make it more fun than typical statistics books. In summary, this is a very interesting introductory book, very well organised and has been written in a style that is extremely pleasant and enjoyable to read. Both the statistical concepts and examples are very well explained. In conclusion, I highly

  8. Analysis of Roadway Traffic Accidents Based on Rough Sets and Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Xiaoxia Xiong

    2018-02-01

    Full Text Available The paper integrates Rough Sets (RS and Bayesian Networks (BN for roadway traffic accident analysis. RS reduction of attributes is first employed to generate the key set of attributes affecting accident outcomes, which are then fed into a BN structure as nodes for BN construction and accident outcome classification. Such RS-based BN framework combines the advantages of RS in knowledge reduction capability and BN in describing interrelationships among different attributes. The framework is demonstrated using the 100-car naturalistic driving data from Virginia Tech Transportation Institute to predict accident type. Comparative evaluation with the baseline BNs shows the RS-based BNs generally have a higher prediction accuracy and lower network complexity while with comparable prediction coverage and receiver operating characteristic curve area, proving that the proposed RS-based BN overall outperforms the BNs with/without traditional feature selection approaches. The proposed RS-based BN indicates the most significant attributes that affect accident types include pre-crash manoeuvre, driver’s attention from forward roadway to centre mirror, number of secondary tasks undertaken, traffic density, and relation to junction, most of which feature pre-crash driver states and driver behaviours that have not been extensively researched in literature, and could give further insight into the nature of traffic accidents.

  9. Analysis of ASR Clogging Investigations at Three Australian ASR Sites in a Bayesian Context

    Directory of Open Access Journals (Sweden)

    Peter Dillon

    2016-10-01

    Full Text Available When evaluating uncertainties in developing an aquifer storage and recovery (ASR system, under normal budgetary constraints, a systematic approach is needed to prioritise investigations. Three case studies where field trials have been undertaken, and clogging evaluated, reveal the changing perceptions of viability of ASR from a clogging perspective as a result of the progress of investigations. Two stormwater and one recycled water ASR investigations in siliceous aquifers are described that involved different strategies to evaluate the potential for clogging. This paper reviews these sites, as well as earlier case studies and information relating water quality, to clogging in column studies. Two novel theoretical concepts are introduced in the paper. Bayesian analysis is applied to demonstrate the increase in expected net benefit in developing a new ASR operation by undertaking clogging experiments (that have an assumed known reliability for predicting viability for the injectant treatment options and aquifer material from the site. Results for an example situation demonstrate benefit cost ratios of experiments ranging from 1.5 to 6 and apply if decisions are based on experimental results whether success or failure are predicted. Additionally, a theoretical assessment of clogging rates characterised as acute and chronic is given, to explore their combined impact, for two operating parameters that define the onset of purging for recovery of reversible clogging and the onset of occasional advanced bore rehabilitation to address recovery of chronic clogging. These allow the assessment of net recharge and the proportion of water purged or redeveloped. Both analyses could inform economic decisions and help motivate an improved investigation methodology. It is expected that aquifer heterogeneity will result in differing injection rates among wells, so operational experience will ultimately be valuable in differentiating clogging behaviour under

  10. Prokinetics for the treatment of functional dyspepsia: Bayesian network meta-analysis.

    Science.gov (United States)

    Yang, Young Joo; Bang, Chang Seok; Baik, Gwang Ho; Park, Tae Young; Shin, Suk Pyo; Suk, Ki Tae; Kim, Dong Joon

    2017-06-26

    Controversies persist regarding the effect of prokinetics for the treatment of functional dyspepsia (FD). This study aimed to assess the comparative efficacy of prokinetic agents for the treatment of FD. Randomized controlled trials (RCTs) of prokinetics for the treatment of FD were identified from core databases. Symptom response rates were extracted and analyzed using odds ratios (ORs). A Bayesian network meta-analysis was performed using the Markov chain Monte Carlo method in WinBUGS and NetMetaXL. In total, 25 RCTs, which included 4473 patients with FD who were treated with 6 different prokinetics or placebo, were identified and analyzed. Metoclopramide showed the best surface under the cumulative ranking curve (SUCRA) probability (92.5%), followed by trimebutine (74.5%) and mosapride (63.3%). However, the therapeutic efficacy of metoclopramide was not significantly different from that of trimebutine (OR:1.32, 95% credible interval: 0.27-6.06), mosapride (OR: 1.99, 95% credible interval: 0.87-4.72), or domperidone (OR: 2.04, 95% credible interval: 0.92-4.60). Metoclopramide showed better efficacy than itopride (OR: 2.79, 95% credible interval: 1.29-6.21) and acotiamide (OR: 3.07, 95% credible interval: 1.43-6.75). Domperidone (SUCRA probability 62.9%) showed better efficacy than itopride (OR: 1.37, 95% credible interval: 1.07-1.77) and acotiamide (OR: 1.51, 95% credible interval: 1.04-2.18). Metoclopramide, trimebutine, mosapride, and domperidone showed better efficacy for the treatment of FD than itopride or acotiamide. Considering the adverse events related to metoclopramide or domperidone, the short-term use of these agents or the alternative use of trimebutine or mosapride could be recommended for the symptomatic relief of FD.

  11. Bayesian Total Error Analysis - An Error Sensitive Approach to Model Calibration

    Science.gov (United States)

    Franks, S. W.; Kavetski, D.; Kuczera, G.

    2002-12-01

    The majority of environmental models require calibration of their parameters before meaningful predictions of catchment behaviour can be made. Despite the importance of reliable parameter estimates, there are growing concerns about the ability of objective-based inference methods to adequately calibrate environmental models. The problem lies with the formulation of the objective or likelihood function, which is currently implemented using essentially ad-hoc methods. We outline limitations of current calibration methodologies and introduce a more systematic Bayesian Total Error Analysis (BATEA) framework for environmental model calibration and validation, which imposes a hitherto missing rigour in environmental modelling by requiring the specification of physically realistic model and data uncertainty models with explicit assumptions that can and must be tested against available evidence. The BATEA formalism enables inference of the hydrological parameters and also of any latent variables of the uncertainty models, e.g., precipitation depth errors. The latter could be useful for improving data sampling and measurement methodologies. In addition, distinguishing between the various sources of errors will reduce the current ambiguity about parameter and predictive uncertainty and enable rational testing of environmental models' hypotheses. Monte Carlo Markov Chain methods are employed to manage the increased computational requirements of BATEA. A case study using synthetic data demonstrates that explicitly accounting for forcing errors leads to immediate advantages over traditional regression (e.g., standard least squares calibration) that ignore rainfall history corruption and pseudo-likelihood methods (e.g., GLUE) do not explicitly characterise data and model errors. It is precisely data and model errors that are responsible for the need for calibration in the first place; we expect that understanding these errors will force fundamental shifts in the model

  12. Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology.

    Science.gov (United States)

    Santra, Tapesh; Kolch, Walter; Kholodenko, Boris N

    2013-07-06

    Recent advancements in genetics and proteomics have led to the acquisition of large quantitative data sets. However, the use of these data to reverse engineer biochemical networks has remained a challenging problem. Many methods have been proposed to infer biochemical network topologies from different types of biological data. Here, we focus on unraveling network topologies from steady state responses of biochemical networks to successive experimental perturbations. We propose a computational algorithm which combines a deterministic network inference method termed Modular Response Analysis (MRA) and a statistical model selection algorithm called Bayesian Variable Selection, to infer functional interactions in cellular signaling pathways and gene regulatory networks. It can be used to identify interactions among individual molecules involved in a biochemical pathway or reveal how different functional modules of a biological network interact with each other to exchange information. In cases where not all network components are known, our method reveals functional interactions which are not direct but correspond to the interaction routes through unknown elements. Using computer simulated perturbation responses of signaling pathways and gene regulatory networks from the DREAM challenge, we demonstrate that the proposed method is robust against noise and scalable to large networks. We also show that our method can infer network topologies using incomplete perturbation datasets. Consequently, we have used this algorithm to explore the ERBB regulated G1/S transition pathway in certain breast cancer cells to understand the molecular mechanisms which cause these cells to become drug resistant. The algorithm successfully inferred many well characterized interactions of this pathway by analyzing experimentally obtained perturbation data. Additionally, it identified some molecular interactions which promote drug resistance in breast cancer cells. The proposed algorithm

  13. A method of spherical harmonic analysis in the geosciences via hierarchical Bayesian inference

    Science.gov (United States)

    Muir, J. B.; Tkalčić, H.

    2015-11-01

    The problem of decomposing irregular data on the sphere into a set of spherical harmonics is common in many fields of geosciences where it is necessary to build a quantitative understanding of a globally varying field. For example, in global seismology, a compressional or shear wave speed that emerges from tomographic images is used to interpret current state and composition of the mantle, and in geomagnetism, secular variation of magnetic field intensity measured at the surface is studied to better understand the changes in the Earth's core. Optimization methods are widely used for spherical harmonic analysis of irregular data, but they typically do not treat the dependence of the uncertainty estimates on the imposed regularization. This can cause significant difficulties in interpretation, especially when the best-fit model requires more variables as a result of underestimating data noise. Here, with the above limitations in mind, the problem of spherical harmonic expansion of irregular data is treated within the hierarchical Bayesian framework. The hierarchical approach significantly simplifies the problem by removing the need for regularization terms and user-supplied noise estimates. The use of the corrected Akaike Information Criterion for picking the optimal maximum degree of spherical harmonic expansion and the resulting spherical harmonic analyses are first illustrated on a noisy synthetic data set. Subsequently, the method is applied to two global data sets sensitive to the Earth's inner core and lowermost mantle, consisting of PKPab-df and PcP-P differential traveltime residuals relative to a spherically symmetric Earth model. The posterior probability distributions for each spherical harmonic coefficient are calculated via Markov Chain Monte Carlo sampling; the uncertainty obtained for the coefficients thus reflects the noise present in the real data and the imperfections in the spherical harmonic expansion.

  14. Bayesian Analysis for Risk Assessment of Selected Medical Events in Support of the Integrated Medical Model Effort

    Science.gov (United States)

    Gilkey, Kelly M.; Myers, Jerry G.; McRae, Michael P.; Griffin, Elise A.; Kallrui, Aditya S.

    2012-01-01

    The Exploration Medical Capability project is creating a catalog of risk assessments using the Integrated Medical Model (IMM). The IMM is a software-based system intended to assist mission planners in preparing for spaceflight missions by helping them to make informed decisions about medical preparations and supplies needed for combating and treating various medical events using Probabilistic Risk Assessment. The objective is to use statistical analyses to inform the IMM decision tool with estimated probabilities of medical events occurring during an exploration mission. Because data regarding astronaut health are limited, Bayesian statistical analysis is used. Bayesian inference combines prior knowledge, such as data from the general U.S. population, the U.S. Submarine Force, or the analog astronaut population located at the NASA Johnson Space Center, with observed data for the medical condition of interest. The posterior results reflect the best evidence for specific medical events occurring in flight. Bayes theorem provides a formal mechanism for combining available observed data with data from similar studies to support the quantification process. The IMM team performed Bayesian updates on the following medical events: angina, appendicitis, atrial fibrillation, atrial flutter, dental abscess, dental caries, dental periodontal disease, gallstone disease, herpes zoster, renal stones, seizure, and stroke.

  15. Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model

    International Nuclear Information System (INIS)

    Chagas Moura, Márcio das; Azevedo, Rafael Valença; Droguett, Enrique López; Chaves, Leandro Rego; Lins, Isis Didier

    2016-01-01

    Occupational accidents pose several negative consequences to employees, employers, environment and people surrounding the locale where the accident takes place. Some types of accidents correspond to low frequency-high consequence (long sick leaves) events, and then classical statistical approaches are ineffective in these cases because the available dataset is generally sparse and contain censored recordings. In this context, we propose a Bayesian population variability method for the estimation of the distributions of the rates of accident and recovery. Given these distributions, a Markov-based model will be used to estimate the uncertainty over the expected number of accidents and the work time loss. Thus, the use of Bayesian analysis along with the Markov approach aims at investigating future trends regarding occupational accidents in a workplace as well as enabling a better management of the labor force and prevention efforts. One application example is presented in order to validate the proposed approach; this case uses available data gathered from a hydropower company in Brazil. - Highlights: • This paper proposes a Bayesian method to estimate rates of accident and recovery. • The model requires simple data likely to be available in the company database. • These results show the proposed model is not too sensitive to the prior estimates.

  16. A Framework for the Statistical Analysis of Probability of Mission Success Based on Bayesian Theory

    Science.gov (United States)

    2014-06-01

    regression, and two non-probabilistic methods, fuzzy logic and neural networks, are discussed and compared below to determine which gives the best...2 2.3 Fuzzy Logic ...accurate measure than possibilistic methods, such as fuzzy logic discussed below [5]. Bayesian inference easily accounts for subjectivity and

  17. Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

    NARCIS (Netherlands)

    Barra, I.; Hoogerheide, L.F.; Koopman, S.J.; Lucas, A.

    2017-01-01

    We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent

  18. Bayesian networks for multivariate data analysis and prognostic modelling in cardiac surgery

    NARCIS (Netherlands)

    Peek, Niels; Verduijn, Marion; Rosseel, Peter M. J.; de Jonge, Evert; de Mol, Bas A.

    2007-01-01

    Prognostic models are tools to predict the outcome of disease and disease treatment. These models are traditionally built with supervised machine learning techniques, and consider prognosis as a static, one-shot activity. This paper presents a new type of prognostic model that builds on the Bayesian

  19. A Bayesian analysis of rare B decays with advanced Monte Carlo methods

    Energy Technology Data Exchange (ETDEWEB)

    Beaujean, Frederik

    2012-11-12

    Searching for new physics in rare B meson decays governed by b {yields} s transitions, we perform a model-independent global fit of the short-distance couplings C{sub 7}, C{sub 9}, and C{sub 10} of the {Delta}B=1 effective field theory. We assume the standard-model set of b {yields} s{gamma} and b {yields} sl{sup +}l{sup -} operators with real-valued C{sub i}. A total of 59 measurements by the experiments BaBar, Belle, CDF, CLEO, and LHCb of observables in B{yields}K{sup *}{gamma}, B{yields}K{sup (*)}l{sup +}l{sup -}, and B{sub s}{yields}{mu}{sup +}{mu}{sup -} decays are used in the fit. Our analysis is the first of its kind to harness the full power of the Bayesian approach to probability theory. All main sources of theory uncertainty explicitly enter the fit in the form of nuisance parameters. We make optimal use of the experimental information to simultaneously constrain theWilson coefficients as well as hadronic form factors - the dominant theory uncertainty. Generating samples from the posterior probability distribution to compute marginal distributions and predict observables by uncertainty propagation is a formidable numerical challenge for two reasons. First, the posterior has multiple well separated maxima and degeneracies. Second, the computation of the theory predictions is very time consuming. A single posterior evaluation requires O(1s), and a few million evaluations are needed. Population Monte Carlo (PMC) provides a solution to both issues; a mixture density is iteratively adapted to the posterior, and samples are drawn in a massively parallel way using importance sampling. The major shortcoming of PMC is the need for cogent knowledge of the posterior at the initial stage. In an effort towards a general black-box Monte Carlo sampling algorithm, we present a new method to extract the necessary information in a reliable and automatic manner from Markov chains with the help of hierarchical clustering. Exploiting the latest 2012 measurements, the fit

  20. Bayesian road safety analysis: incorporation of past evidence and effect of hyper-prior choice.

    Science.gov (United States)

    Miranda-Moreno, Luis F; Heydari, Shahram; Lord, Dominique; Fu, Liping

    2013-09-01

    This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites). A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas. This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis. As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification. The results show the benefits of incorporating prior

  1. Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions.

    Science.gov (United States)

    Vernon, Ian; Liu, Junli; Goldstein, Michael; Rowe, James; Topping, Jen; Lindsey, Keith

    2018-01-02

    Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology. Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embeded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. The approach is demonstrated via application to a model of hormonal crosstalk in Arabidopsis root development, which has 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches between model output and observed trend data. The multiple insights into the model's structure that this analysis provides are discussed. The methodology is applied to a second related model, and the biological consequences of the resulting comparison, including the evaluation of gene functions, are described. Bayesian uncertainty analysis for complex models using both emulators and history matching is shown to be a powerful technique that can greatly aid the study of a large class of systems biology models. It both provides insight into model behaviour

  2. Apples and oranges: avoiding different priors in Bayesian DNA sequence analysis

    Directory of Open Access Journals (Sweden)

    Posch Stefan

    2010-03-01

    Full Text Available Abstract Background One of the challenges of bioinformatics remains the recognition of short signal sequences in genomic DNA such as donor or acceptor splice sites, splicing enhancers or silencers, translation initiation sites, transcription start sites, transcription factor binding sites, nucleosome binding sites, miRNA binding sites, or insulator binding sites. During the last decade, a wealth of algorithms for the recognition of such DNA sequences has been developed and compared with the goal of improving their performance and to deepen our understanding of the underlying cellular processes. Most of these algorithms are based on statistical models belonging to the family of Markov random fields such as position weight matrix models, weight array matrix models, Markov models of higher order, or moral Bayesian networks. While in many comparative studies different learning principles or different statistical models have been compared, the influence of choosing different prior distributions for the model parameters when using different learning principles has been overlooked, and possibly lead to questionable conclusions. Results With the goal of allowing direct comparisons of different learning principles for models from the family of Markov random fields based on the same a-priori information, we derive a generalization of the commonly-used product-Dirichlet prior. We find that the derived prior behaves like a Gaussian prior close to the maximum and like a Laplace prior in the far tails. In two case studies, we illustrate the utility of the derived prior for a direct comparison of different learning principles with different models for the recognition of binding sites of the transcription factor Sp1 and human donor splice sites. Conclusions We find that comparisons of different learning principles using the same a-priori information can lead to conclusions different from those of previous studies in which the effect resulting from different

  3. A Bayesian ridge regression analysis of congestion's impact on urban expressway safety.

    Science.gov (United States)

    Shi, Qi; Abdel-Aty, Mohamed; Lee, Jaeyoung

    2016-03-01

    With the rapid growth of traffic in urban areas, concerns about congestion and traffic safety have been heightened. This study leveraged both Automatic Vehicle Identification (AVI) system and Microwave Vehicle Detection System (MVDS) installed on an expressway in Central Florida to explore how congestion impacts the crash occurrence in urban areas. Multiple congestion measures from the two systems were developed. To ensure more precise estimates of the congestion's effects, the traffic data were aggregated into peak and non-peak hours. Multicollinearity among traffic parameters was examined. The results showed the presence of multicollinearity especially during peak hours. As a response, ridge regression was introduced to cope with this issue. Poisson models with uncorrelated random effects, correlated random effects, and both correlated random effects and random parameters were constructed within the Bayesian framework. It was proven that correlated random effects could significantly enhance model performance. The random parameters model has similar goodness-of-fit compared with the model with only correlated random effects. However, by accounting for the unobserved heterogeneity, more variables were found to be significantly related to crash frequency. The models indicated that congestion increased crash frequency during peak hours while during non-peak hours it was not a major crash contributing factor. Using the random parameter model, the three congestion measures were compared. It was found that all congestion indicators had similar effects while Congestion Index (CI) derived from MVDS data was a better congestion indicator for safety analysis. Also, analyses showed that the segments with higher congestion intensity could not only increase property damage only (PDO) crashes, but also more severe crashes. In addition, the issues regarding the necessity to incorporate specific congestion indicator for congestion's effects on safety and to take care of the

  4. Neglected chaos in international stock markets: Bayesian analysis of the joint return-volatility dynamical system

    Science.gov (United States)

    Tsionas, Mike G.; Michaelides, Panayotis G.

    2017-09-01

    We use a novel Bayesian inference procedure for the Lyapunov exponent in the dynamical system of returns and their unobserved volatility. In the dynamical system, computation of largest Lyapunov exponent by traditional methods is impossible as the stochastic nature has to be taken explicitly into account due to unobserved volatility. We apply the new techniques to daily stock return data for a group of six countries, namely USA, UK, Switzerland, Netherlands, Germany and France, from 2003 to 2014, by means of Sequential Monte Carlo for Bayesian inference. The evidence points to the direction that there is indeed noisy chaos both before and after the recent financial crisis. However, when a much simpler model is examined where the interaction between returns and volatility is not taken into consideration jointly, the hypothesis of chaotic dynamics does not receive much support by the data ("neglected chaos").

  5. A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies

    International Nuclear Information System (INIS)

    Chen, Zhongfei; Barros, Carlos Pestana; Borges, Maria Rosa

    2015-01-01

    This paper analyses the technical efficiency of Chinese fossil-fuel electricity generation companies from 1999 to 2011, using a Bayesian stochastic frontier model. The results reveal that efficiency varies among the fossil-fuel electricity generation companies that were analysed. We also focus on the factors of size, location, government ownership and mixed sources of electricity generation for the fossil-fuel electricity generation companies, and also examine their effects on the efficiency of these companies. Policy implications are derived. - Highlights: • We analyze the efficiency of 27 quoted Chinese fossil-fuel electricity generation companies during 1999–2011. • We adopt a Bayesian stochastic frontier model taking into consideration the identified heterogeneity. • With reform background in Chinese energy industry, we propose four hypotheses and check their influence on efficiency. • Big size, coastal location, government control and hydro energy sources all have increased costs

  6. Time independent seismic hazard analysis of Greece deduced from Bayesian statistics

    Directory of Open Access Journals (Sweden)

    T. M. Tsapanos

    2003-01-01

    Full Text Available A Bayesian statistics approach is applied in the seismogenic sources of Greece and the surrounding area in order to assess seismic hazard, assuming that the earthquake occurrence follows the Poisson process. The Bayesian approach applied supplies the probability that a certain cut-off magnitude of Ms = 6.0 will be exceeded in time intervals of 10, 20 and 75 years. We also produced graphs which present the different seismic hazard in the seismogenic sources examined in terms of varying probability which is useful for engineering and civil protection purposes, allowing the designation of priority sources for earthquake-resistant design. It is shown that within the above time intervals the seismogenic source (4 called Igoumenitsa (in NW Greece and west Albania has the highest probability to experience an earthquake with magnitude M > 6.0. High probabilities are found also for Ochrida (source 22, Samos (source 53 and Chios (source 56.

  7. Bus Route Design with a Bayesian Network Analysis of Bus Service Revenues

    Directory of Open Access Journals (Sweden)

    Yi Liu

    2018-01-01

    Full Text Available A Bayesian network is used to estimate revenues of bus services in consideration of the effect of bus travel demands, passenger transport distances, and so on. In this research, the area X in Beijing has been selected as the study area because of its relatively high bus travel demand and, on the contrary, unsatisfactory bus services. It is suggested that the proposed Bayesian network approach is able to rationally predict the probabilities of different revenues of various route services, from the perspectives of both satisfying passenger demand and decreasing bus operation cost. This way, the existing bus routes in the studied area can be optimized for their most probable high revenues.

  8. PARALLEL ADAPTIVE MULTILEVEL SAMPLING ALGORITHMS FOR THE BAYESIAN ANALYSIS OF MATHEMATICAL MODELS

    KAUST Repository

    Prudencio, Ernesto

    2012-01-01

    In recent years, Bayesian model updating techniques based on measured data have been applied to many engineering and applied science problems. At the same time, parallel computational platforms are becoming increasingly more powerful and are being used more frequently by the engineering and scientific communities. Bayesian techniques usually require the evaluation of multi-dimensional integrals related to the posterior probability density function (PDF) of uncertain model parameters. The fact that such integrals cannot be computed analytically motivates the research of stochastic simulation methods for sampling posterior PDFs. One such algorithm is the adaptive multilevel stochastic simulation algorithm (AMSSA). In this paper we discuss the parallelization of AMSSA, formulating the necessary load balancing step as a binary integer programming problem. We present a variety of results showing the effectiveness of load balancing on the overall performance of AMSSA in a parallel computational environment.

  9. An analysis on operational risk in international banking: A Bayesian approach (2007–2011

    Directory of Open Access Journals (Sweden)

    José Francisco Martínez-Sánchez

    2016-07-01

    Full Text Available This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk in several business lines of commercial banking. To do this, a Bayesian network (BN model is designed with prior and subsequent distributions to estimate the frequency and severity. Regarding the subsequent distributions, an inference procedure for the maximum expected loss, for a period of 20 days, is carried out by using the Monte Carlo simulation method. The business lines analyzed are marketing and sales, retail banking and private banking, which all together accounted for 88.5% of the losses in 2011. Data was obtained for the period 2007–2011 from the Riskdata Operational Exchange Association (ORX, and external data was provided from qualified experts to complete the missing records or to improve its poor quality.

  10. Limitations of cytochrome oxidase I for the barcoding of Neritidae (Mollusca: Gastropoda) as revealed by Bayesian analysis.

    Science.gov (United States)

    Chee, S Y

    2015-05-25

    The mitochondrial DNA (mtDNA) cytochrome oxidase I (COI) gene has been universally and successfully utilized as a barcoding gene, mainly because it can be amplified easily, applied across a wide range of taxa, and results can be obtained cheaply and quickly. However, in rare cases, the gene can fail to distinguish between species, particularly when exposed to highly sensitive methods of data analysis, such as the Bayesian method, or when taxa have undergone introgressive hybridization, over-splitting, or incomplete lineage sorting. Such cases require the use of alternative markers, and nuclear DNA markers are commonly used. In this study, a dendrogram produced by Bayesian analysis of an mtDNA COI dataset was compared with that of a nuclear DNA ATPS-α dataset, in order to evaluate the efficiency of COI in barcoding Malaysian nerites (Neritidae). In the COI dendrogram, most of the species were in individual clusters, except for two species: Nerita chamaeleon and N. histrio. These two species were placed in the same subcluster, whereas in the ATPS-α dendrogram they were in their own subclusters. Analysis of the ATPS-α gene also placed the two genera of nerites (Nerita and Neritina) in separate clusters, whereas COI gene analysis placed both genera in the same cluster. Therefore, in the case of the Neritidae, the ATPS-α gene is a better barcoding gene than the COI gene.

  11. Spatial Intensity Duration Frequency Relationships Using Hierarchical Bayesian Analysis for Urban Areas

    Science.gov (United States)

    Rupa, Chandra; Mujumdar, Pradeep

    2016-04-01

    In urban areas, quantification of extreme precipitation is important in the design of storm water drains and other infrastructure. Intensity Duration Frequency (IDF) relationships are generally used to obtain design return level for a given duration and return period. Due to lack of availability of extreme precipitation data for sufficiently large number of years, estimating the probability of extreme events is difficult. Typically, a single station data is used to obtain the design return levels for various durations and return periods, which are used in the design of urban infrastructure for the entire city. In an urban setting, the spatial variation of precipitation can be high; the precipitation amounts and patterns often vary within short distances of less than 5 km. Therefore it is crucial to study the uncertainties in the spatial variation of return levels for various durations. In this work, the extreme precipitation is modeled spatially using the Bayesian hierarchical analysis and the spatial variation of return levels is studied. The analysis is carried out with Block Maxima approach for defining the extreme precipitation, using Generalized Extreme Value (GEV) distribution for Bangalore city, Karnataka state, India. Daily data for nineteen stations in and around Bangalore city is considered in the study. The analysis is carried out for summer maxima (March - May), monsoon maxima (June - September) and the annual maxima rainfall. In the hierarchical analysis, the statistical model is specified in three layers. The data layer models the block maxima, pooling the extreme precipitation from all the stations. In the process layer, the latent spatial process characterized by geographical and climatological covariates (lat-lon, elevation, mean temperature etc.) which drives the extreme precipitation is modeled and in the prior level, the prior distributions that govern the latent process are modeled. Markov Chain Monte Carlo (MCMC) algorithm (Metropolis Hastings

  12. BAYESIAN ANALYSIS FOR THE PAIRED COMPARISON MODEL WITH ORDER EFFECTS (USING NON-INFORMATIVE PRIORS

    Directory of Open Access Journals (Sweden)

    Ghausia Masood Gilani

    2008-07-01

    Full Text Available Sometimes it may be difficult for a panelist to rank or compare more than two objects or treatments at the same time. For this reason, paired comparison method is used. In this study, the Davidson and Beaver (1977 model for paired comparisons with order effects is analyzed through the Bayesian Approach. For this purpose, the posterior means and the posterior modes are compared using the noninformative priors.

  13. The Directional Identification Problem in Bayesian Factor Analysis: An Ex-Post Approach

    OpenAIRE

    Pape, Markus; Aßmann, Christian; Boysen-Hogrefe, Jens

    2013-01-01

    Due to their well-known indeterminacies, factor models require identifying assumptions to guarantee unique parameter estimates. For Bayesian estimation, these identifying assumptions are usually implemented by imposing constraints on certain model parameters. This strategy, however, may result in posterior distributions with shapes that depend on the ordering of cross-sections in the data set. We propose an alternative approach, which relies on a sampler without the usual identifying constrai...

  14. Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.

    Directory of Open Access Journals (Sweden)

    Xiaodong Huang

    Full Text Available Nitrous oxide (N2O is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2 field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.

  15. Bayesian networks and statistical analysis application to analyze the diagnostic test accuracy

    Science.gov (United States)

    Orzechowski, P.; Makal, Jaroslaw; Onisko, A.

    2005-02-01

    The computer aided BPH diagnosis system based on Bayesian network is described in the paper. First result are compared to a given statistical method. Different statistical methods are used successfully in medicine for years. However, the undoubted advantages of probabilistic methods make them useful in application in newly created systems which are frequent in medicine, but do not have full and competent knowledge. The article presents advantages of the computer aided BPH diagnosis system in clinical practice for urologists.

  16. Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis.

    Science.gov (United States)

    Sharpe, J Danielle; Hopkins, Richard S; Cook, Robert L; Striley, Catherine W

    2016-10-20

    Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods. The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter. Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC's change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package "bcp" version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. During the 2012-2015 influenza seasons, a high sensitivity of 92% was found for Google, whereas the PPV for

  17. An imprecise Dirichlet model for Bayesian analysis of failure data including right-censored observations

    International Nuclear Information System (INIS)

    Coolen, F.P.A.

    1997-01-01

    This paper is intended to make researchers in reliability theory aware of a recently introduced Bayesian model with imprecise prior distributions for statistical inference on failure data, that can also be considered as a robust Bayesian model. The model consists of a multinomial distribution with Dirichlet priors, making the approach basically nonparametric. New results for the model are presented, related to right-censored observations, where estimation based on this model is closely related to the product-limit estimator, which is an important statistical method to deal with reliability or survival data including right-censored observations. As for the product-limit estimator, the model considered in this paper aims at not using any information other than that provided by observed data, but our model fits into the robust Bayesian context which has the advantage that all inferences can be based on probabilities or expectations, or bounds for probabilities or expectations. The model uses a finite partition of the time-axis, and as such it is also related to life-tables

  18. Bayesian analysis of the astrobiological implications of life's early emergence on Earth.

    Science.gov (United States)

    Spiegel, David S; Turner, Edwin L

    2012-01-10

    Life arose on Earth sometime in the first few hundred million years after the young planet had cooled to the point that it could support water-based organisms on its surface. The early emergence of life on Earth has been taken as evidence that the probability of abiogenesis is high, if starting from young Earth-like conditions. We revisit this argument quantitatively in a bayesian statistical framework. By constructing a simple model of the probability of abiogenesis, we calculate a bayesian estimate of its posterior probability, given the data that life emerged fairly early in Earth's history and that, billions of years later, curious creatures noted this fact and considered its implications. We find that, given only this very limited empirical information, the choice of bayesian prior for the abiogenesis probability parameter has a dominant influence on the computed posterior probability. Although terrestrial life's early emergence provides evidence that life might be abundant in the universe if early-Earth-like conditions are common, the evidence is inconclusive and indeed is consistent with an arbitrarily low intrinsic probability of abiogenesis for plausible uninformative priors. Finding a single case of life arising independently of our lineage (on Earth, elsewhere in the solar system, or on an extrasolar planet) would provide much stronger evidence that abiogenesis is not extremely rare in the universe.

  19. Bayesian Inference on Gravitational Waves

    Directory of Open Access Journals (Sweden)

    Asad Ali

    2015-12-01

    Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an  overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.

  20. Risk Analysis on Leakage Failure of Natural Gas Pipelines by Fuzzy Bayesian Network with a Bow-Tie Model

    Directory of Open Access Journals (Sweden)

    Xian Shan

    2017-01-01

    Full Text Available Pipeline is the major mode of natural gas transportation. Leakage of natural gas pipelines may cause explosions and fires, resulting in casualties, environmental damage, and material loss. Efficient risk analysis is of great significance for preventing and mitigating such potential accidents. The objective of this study is to present a practical risk assessment method based on Bow-tie model and Bayesian network for risk analysis of natural gas pipeline leakage. Firstly, identify the potential risk factors and consequences of the failure. Then construct the Bow-tie model, use the quantitative analysis of Bayesian network to find the weak links in the system, and make a prediction of the control measures to reduce the rate of the accident. In order to deal with the uncertainty existing in the determination of the probability of basic events, fuzzy logic method is used. Results of a case study show that the most likely causes of natural gas pipeline leakage occurrence are parties ignore signage, implicit signage, overload, and design defect of auxiliaries. Once the leakage occurs, it is most likely to result in fire and explosion. Corresponding measures taken on time will reduce the disaster degree of accidents to the least extent.

  1. Cervical disc arthroplasty for symptomatic cervical disc disease: Traditional and Bayesian meta-analysis with trial sequential analysis.

    Science.gov (United States)

    Kan, Shun-Li; Yuan, Zhi-Fang; Ning, Guang-Zhi; Liu, Fei-Fei; Sun, Jing-Cheng; Feng, Shi-Qing

    2016-11-01

    Cervical disc arthroplasty (CDA) has been designed as a substitute for anterior cervical discectomy and fusion (ACDF) in the treatment of symptomatic cervical disc disease (CDD). Several researchers have compared CDA with ACDF for the treatment of symptomatic CDD; however, the findings of these studies are inconclusive. Using recently published evidence, this meta-analysis was conducted to further verify the benefits and harms of using CDA for treatment of symptomatic CDD. Relevant trials were identified by searching the PubMed, EMBASE, and Cochrane Library databases. Outcomes were reported as odds ratio or standardized mean difference. Both traditional frequentist and Bayesian approaches were used to synthesize evidence within random-effects models. Trial sequential analysis (TSA) was applied to test the robustness of our findings and obtain more conservative estimates. Nineteen trials were included. The findings of this meta-analysis demonstrated better overall, neck disability index (NDI), and neurological success; lower NDI and neck and arm pain scores; higher 36-Item Short Form Health Survey (SF-36) Physical Component Summary (PCS) and Mental Component Summary (MCS) scores; more patient satisfaction; greater range of motion at the operative level; and fewer secondary surgical procedures (all P  0.05). TSA of overall success suggested that the cumulative z-curve crossed both the conventional boundary and the trial sequential monitoring boundary for benefit, indicating sufficient and conclusive evidence had been ascertained. For treating symptomatic CDD, CDA was superior to ACDF in terms of overall, NDI, and neurological success; NDI and neck and arm pain scores; SF-36 PCS and MCS scores; patient satisfaction; ROM at the operative level; and secondary surgical procedures rate. Additionally, there was no significant difference between CDA and ACDF in the rate of adverse events. However, as the CDA procedure is a relatively newer operative technique, long

  2. A Bayesian technique for improving the sensitivity of the atmospheric neutrino L/E analysis

    Energy Technology Data Exchange (ETDEWEB)

    Blake, A. S. T. [Univ. of Cambridge (United Kingdom); Chapman, J. D. [Univ. of Cambridge (United Kingdom); Thomson, M. A. [Univ. of Cambridge (United Kingdom)

    2013-04-01

    here, a Bayesian technique is used to estimate the Lν/Eν resolution of observed atmospheric neutrinos on an event-by-event basis. By separating the events into bins of Lν/Eν resolution in the oscillation analysis, a significant improvement in oscillation sensitivity can be achieved.

  3. Application of Bayesian configural frequency analysis (BCFA) to determine characteristics user and non-user motor X

    Science.gov (United States)

    Mawardi, Muhamad Iqbal; Padmadisastra, Septiadi; Tantular, Bertho

    2018-03-01

    Configural Frequency Analysis is a method for cell-wise testing in contingency tables for exploratory search type and antitype, that can see the existence of discrepancy on the model by existence of a significant difference between the frequency of observation and frequency of expectation. This analysis focuses on whether or not the interaction among categories from different variables, and not the interaction among variables. One of the extensions of CFA method is Bayesian CFA, this alternative method pursue the same goal as frequentist version of CFA with the advantage that adjustment of the experiment-wise significance level α is not necessary and test whether groups of types and antitypes form composite types or composite antitypes. Hence, this research will present the concept of the Bayesian CFA and how it works for the real data. The data on this paper is based on case studies in a company about decrease Brand Awareness & Image motor X on Top Of Mind Unit indicator in Cirebon City for user 30.8% and non user 9.8%. From the result of B-CFA have four characteristics from deviation, one of the four characteristics above that is the configuration 2212 need more attention by company to determine promotion strategy to maintain and improve Top Of Mind Unit in Cirebon City.

  4. Joint High-Dimensional Bayesian Variable and Covariance Selection with an Application to eQTL Analysis

    KAUST Repository

    Bhadra, Anindya

    2013-04-22

    We describe a Bayesian technique to (a) perform a sparse joint selection of significant predictor variables and significant inverse covariance matrix elements of the response variables in a high-dimensional linear Gaussian sparse seemingly unrelated regression (SSUR) setting and (b) perform an association analysis between the high-dimensional sets of predictors and responses in such a setting. To search the high-dimensional model space, where both the number of predictors and the number of possibly correlated responses can be larger than the sample size, we demonstrate that a marginalization-based collapsed Gibbs sampler, in combination with spike and slab type of priors, offers a computationally feasible and efficient solution. As an example, we apply our method to an expression quantitative trait loci (eQTL) analysis on publicly available single nucleotide polymorphism (SNP) and gene expression data for humans where the primary interest lies in finding the significant associations between the sets of SNPs and possibly correlated genetic transcripts. Our method also allows for inference on the sparse interaction network of the transcripts (response variables) after accounting for the effect of the SNPs (predictor variables). We exploit properties of Gaussian graphical models to make statements concerning conditional independence of the responses. Our method compares favorably to existing Bayesian approaches developed for this purpose. © 2013, The International Biometric Society.

  5. Uncertainty analysis of pollutant build-up modelling based on a Bayesian weighted least squares approach

    International Nuclear Information System (INIS)

    Haddad, Khaled; Egodawatta, Prasanna; Rahman, Ataur; Goonetilleke, Ashantha

    2013-01-01

    Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality datasets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares regression and Bayesian weighted least squares regression for the estimation of uncertainty associated with pollutant build-up prediction using limited datasets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling. - Highlights: ► Water quality data spans short time scales leading to significant model uncertainty. ► Assessment of uncertainty essential for informed decision making in water

  6. BAYESIAN APPROACH TO THE ANALYSIS OF MONETARY POLICY IMPACT ON RUSSIAN MACROECONOMICS INDICATORS

    Directory of Open Access Journals (Sweden)

    Sheveleva O. A.

    2017-12-01

    Full Text Available In this paper the interaction between the production macroeconomic indicators of the Russian economy and MIBOR (the main operational benchmark of the Bank of Russia, as well as the relationship between the inflation indicators and money supply were investigated with Bayesian approach. Conjugate Normal Inverse Wishart Prior was used. According to the study, tough monetary policy has a deterrent effect on the Russian economy. The growth of the money market rate causes a reduction in investments and output in the main sectors of the economy, as well as a drop in the income of the population with an increase in the unemployment rate.

  7. A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models.

    Science.gov (United States)

    Fronczyk, Kassandra; Kottas, Athanasios

    2014-03-01

    We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose-response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose-response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature. © 2013, The International Biometric Society.

  8. Small-signal analysis in high-energy physics: A Bayesian approach

    International Nuclear Information System (INIS)

    Prosper, H.B.

    1988-01-01

    The statistics of small signals masked by a background of imprecisely known magnitude is addressed from a Bayesian viewpoint using a simple statistical model which may be derived from the principle of maximum entropy. The issue of the correct assignment of prior probabilities is resolved by invoking an invariance principle proposed by Jaynes. We calculate the posterior probability and use it to calculate point estimates and upper limits for the magnitude of the signal. The results are applicable to high-energy physics experiments searching for new phenomena. We illustrate this by reanalyzing some published data from a few experiments

  9. Decision-theoretic analysis of forensic sampling criteria using bayesian decision networks.

    Science.gov (United States)

    Biedermann, A; Bozza, S; Garbolino, P; Taroni, F

    2012-11-30

    Sampling issues represent a topic of ongoing interest to the forensic science community essentially because of their crucial role in laboratory planning and working protocols. For this purpose, forensic literature described thorough (bayesian) probabilistic sampling approaches. These are now widely implemented in practice. They allow, for instance, to obtain probability statements that parameters of interest (e.g., the proportion of a seizure of items that present particular features, such as an illegal substance) satisfy particular criteria (e.g., a threshold or an otherwise limiting value). Currently, there are many approaches that allow one to derive probability statements relating to a population proportion, but questions on how a forensic decision maker--typically a client of a forensic examination or a scientist acting on behalf of a client--ought actually to decide about a proportion or a sample size, remained largely unexplored to date. The research presented here intends to address methodology from decision theory that may help to cope usefully with the wide range of sampling issues typically encountered in forensic science applications. The procedures explored in this paper enable scientists to address a variety of concepts such as the (net) value of sample information, the (expected) value of sample information or the (expected) decision loss. All of these aspects directly relate to questions that are regularly encountered in casework. Besides probability theory and bayesian inference, the proposed approach requires some additional elements from decision theory that may increase the efforts needed for practical implementation. In view of this challenge, the present paper will emphasise the merits of graphical modelling concepts, such as decision trees and bayesian decision networks. These can support forensic scientists in applying the methodology in practice. How this may be achieved is illustrated with several examples. The graphical devices invoked

  10. Qubit feedback and control with kicked quantum nondemolition measurements: A quantum Bayesian analysis

    Science.gov (United States)

    Jordan, Andrew N.; Korotkov, Alexander N.

    2006-08-01

    The informational approach to continuous quantum measurement is derived from positive operator-valued measure formalism for a mesoscopic scattering detector measuring a charge qubit. Quantum Bayesian equations for the qubit density matrix are derived, and cast into the form of a stochastic conformal map. Measurement statistics are derived for kicked quantum nondemolition measurements, combined with conditional unitary operations. These results are applied to derive a feedback protocol to produce an arbitrary pure state after a weak measurement, as well as to investigate how an initially mixed state becomes purified with and without feedback.

  11. Bayesian analysis of spatial point processes in the neighbourhood of Voronoi networks

    DEFF Research Database (Denmark)

    Skare, Øivind; Møller, Jesper; Vedel Jensen, Eva B.

    A model for an inhomogeneous Poisson process with high intensity near the edges of a Voronoi tessellation in 2D or 3D is proposed. The model is analysed in a Bayesian setting with priors on nuclei of the Voronoi tessellation and other model parameters. An MCMC algorithm is constructed to sample...... from the posterior, which contains information about the unobserved Voronoi tessellation and the model parameters. A major element of the MCMC algorithm is the reconstruction of the Voronoi tessellation after a proposed local change of the tessellation. A simulation study and examples of applications...

  12. Bayesian analysis of spatial point processes in the neighbourhood of Voronoi networks

    DEFF Research Database (Denmark)

    Skare, Øivind; Møller, Jesper; Jensen, Eva Bjørn Vedel

    2007-01-01

    A model for an inhomogeneous Poisson process with high intensity near the edges of a Voronoi tessellation in 2D or 3D is proposed. The model is analysed in a Bayesian setting with priors on nuclei of the Voronoi tessellation and other model parameters. An MCMC algorithm is constructed to sample...... from the posterior, which contains information about the unobserved Voronoi tessellation and the model parameters. A major element of the MCMC algorithm is the reconstruction of the Voronoi tessellation after a proposed local change of the tessellation. A simulation study and examples of applications...

  13. Attributes of GRB pulses: Bayesian blocks analysis of TTE data; a microburst in GRB920229

    Science.gov (United States)

    Scargle, Jeffrey D.; Norris, Jay; Bonnell, Jerry

    1998-05-01

    Bayesian Blocks is a new time series algorithm for detecting localized structures (spikes or shots), revealing pulse shapes, and generally characterizing intensity variations. It maps raw counting data into a maximum likelihood piecewise constant representation of the underlying signal. This bin-free method imposes no lower limit on measurable time scales. Applied to BATSE TTE data, it reveals the shortest known burst structure-a spike superimposed on the main burst in GRB920229 (BATSE trigger 1453), with rise and decay timescales~few 100 μs.

  14. Bayesian optimization analysis of containment-venting operation in a boiling water reactor severe accident

    Energy Technology Data Exchange (ETDEWEB)

    Zheng, Xiaoyu; Ishikawa, Jun; Sugiyama, Tomoyuki; Maryyama, Yu [Nuclear Safety Research Center, Japan Atomic Energy Agency, Ibaraki (Japan)

    2017-03-15

    Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the “black-box” code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.

  15. Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis

    Directory of Open Access Journals (Sweden)

    Moslem Moradi

    2015-06-01

    Full Text Available Here in, an application of a new seismic inversion algorithm in one of Iran’s oilfields is described. Stochastic (geostatistical seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. This method integrates information from different data sources with different scales, as prior information in Bayesian statistics. Data integration leads to a probability density function (named as a posteriori probability that can yield a model of subsurface. The Markov Chain Monte Carlo (MCMC method is used to sample the posterior probability distribution, and the subsurface model characteristics can be extracted by analyzing a set of the samples. In this study, the theory of stochastic seismic inversion in a Bayesian framework was described and applied to infer P-impedance and porosity models. The comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more detailed information of subsurface character. Since multiple realizations are extracted by this method, an estimation of pore volume and uncertainty in the estimation were analyzed.

  16. Bayesian analysis of a morphological supermatrix sheds light on controversial fossil hominin relationships.

    Science.gov (United States)

    Dembo, Mana; Matzke, Nicholas J; Mooers, Arne Ø; Collard, Mark

    2015-08-07

    The phylogenetic relationships of several hominin species remain controversial. Two methodological issues contribute to the uncertainty-use of partial, inconsistent datasets and reliance on phylogenetic methods that are ill-suited to testing competing hypotheses. Here, we report a study designed to overcome these issues. We first compiled a supermatrix of craniodental characters for all widely accepted hominin species. We then took advantage of recently developed Bayesian methods for building trees of serially sampled tips to test among hypotheses that have been put forward in three of the most important current debates in hominin phylogenetics--the relationship between Australopithecus sediba and Homo, the taxonomic status of the Dmanisi hominins, and the place of the so-called hobbit fossils from Flores, Indonesia, in the hominin tree. Based on our results, several published hypotheses can be statistically rejected. For example, the data do not support the claim that Dmanisi hominins and all other early Homo specimens represent a single species, nor that the hobbit fossils are the remains of small-bodied modern humans, one of whom had Down syndrome. More broadly, our study provides a new baseline dataset for future work on hominin phylogeny and illustrates the promise of Bayesian approaches for understanding hominin phylogenetic relationships. © 2015 The Author(s).

  17. Bayesian approach to MSD-based analysis of particle motion in live cells.

    Science.gov (United States)

    Monnier, Nilah; Guo, Syuan-Ming; Mori, Masashi; He, Jun; Lénárt, Péter; Bathe, Mark

    2012-08-08

    Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  18. Bayesian models for cost-effectiveness analysis in the presence of structural zero costs.

    Science.gov (United States)

    Baio, Gianluca

    2014-05-20

    Bayesian modelling for cost-effectiveness data has received much attention in both the health economics and the statistical literature, in recent years. Cost-effectiveness data are characterised by a relatively complex structure of relationships linking a suitable measure of clinical benefit (e.g. quality-adjusted life years) and the associated costs. Simplifying assumptions, such as (bivariate) normality of the underlying distributions, are usually not granted, particularly for the cost variable, which is characterised by markedly skewed distributions. In addition, individual-level data sets are often characterised by the presence of structural zeros in the cost variable. Hurdle models can be used to account for the presence of excess zeros in a distribution and have been applied in the context of cost data. We extend their application to cost-effectiveness data, defining a full Bayesian specification, which consists of a model for the individual probability of null costs, a marginal model for the costs and a conditional model for the measure of effectiveness (given the observed costs). We presented the model using a working example to describe its main features. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

  19. Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model.

    Directory of Open Access Journals (Sweden)

    Gerhard Moser

    2015-04-01

    Full Text Available Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96% had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.

  20. Semiparametric Bayesian Analysis of Nutritional Epidemiology Data in the Presence of Measurement Error

    KAUST Repository

    Sinha, Samiran

    2009-08-10

    We propose a semiparametric Bayesian method for handling measurement error in nutritional epidemiological data. Our goal is to estimate nonparametrically the form of association between a disease and exposure variable while the true values of the exposure are never observed. Motivated by nutritional epidemiological data, we consider the setting where a surrogate covariate is recorded in the primary data, and a calibration data set contains information on the surrogate variable and repeated measurements of an unbiased instrumental variable of the true exposure. We develop a flexible Bayesian method where not only is the relationship between the disease and exposure variable treated semiparametrically, but also the relationship between the surrogate and the true exposure is modeled semiparametrically. The two nonparametric functions are modeled simultaneously via B-splines. In addition, we model the distribution of the exposure variable as a Dirichlet process mixture of normal distributions, thus making its modeling essentially nonparametric and placing this work into the context of functional measurement error modeling. We apply our method to the NIH-AARP Diet and Health Study and examine its performance in a simulation study.

  1. BayesWHAM: A Bayesian approach for free energy estimation, reweighting, and uncertainty quantification in the weighted histogram analysis method.

    Science.gov (United States)

    Ferguson, Andrew L

    2017-07-05

    The weighted histogram analysis method (WHAM) is a powerful approach to estimate molecular free energy surfaces (FES) from biased simulation data. Bayesian reformulations of WHAM are valuable in proving statistically optimal use of the data and providing a transparent means to incorporate regularizing priors and estimate statistical uncertainties. In this work, we develop a fully Bayesian treatment of WHAM to generate statistically optimal FES estimates in any number of biasing dimensions under arbitrary choices of the Bayes prior. Rigorous uncertainty estimates are generated by Metropolis-Hastings sampling from the Bayes posterior. We also report a means to project the FES and its uncertainties into arbitrary auxiliary order parameters beyond those in which biased sampling was conducted. We demonstrate the approaches in applications of alanine dipeptide and the unthreading of a synthetic mimic of the astexin-3 lasso peptide. Open-source MATLAB and Python implementations of our codes are available for free public download. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  2. Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach

    International Nuclear Information System (INIS)

    Cheng, Yujie; Lu, Chen; Li, Tieying; Tao, Laifa

    2015-01-01

    Existing methods for predicting lithium-ion (Li-ion) battery residual lifetime mostly depend on a priori knowledge on aging mechanism, the use of chemical or physical formulation and analytical battery models. This dependence is usually difficult to determine in practice, which restricts the application of these methods. In this study, we propose a new prediction method for Li-ion battery residual lifetime evaluation based on FPCA (functional principal component analysis) and Bayesian approach. The proposed method utilizes FPCA to construct a nonparametric degradation model for Li-ion battery, based on which the residual lifetime and the corresponding confidence interval can be evaluated. Furthermore, an empirical Bayes approach is utilized to achieve real-time updating of the degradation model and concurrently determine residual lifetime distribution. Based on Bayesian updating, a more accurate prediction result and a more precise confidence interval are obtained. Experiments are implemented based on data provided by the NASA Ames Prognostics Center of Excellence. Results confirm that the proposed prediction method performs well in real-time battery residual lifetime prediction. - Highlights: • Capacity is considered functional and FPCA is utilized to extract more information. • No features required which avoids drawbacks induced by feature extraction. • A good combination of both population and individual information. • Avoiding complex aging mechanism and accurate analytical models of batteries. • Easily applicable to different batteries for life prediction and RLD calculation.

  3. Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application

    International Nuclear Information System (INIS)

    Baraldi, Piero; Podofillini, Luca; Mkrtchyan, Lusine; Zio, Enrico; Dang, Vinh N.

    2015-01-01

    The use of expert systems can be helpful to improve the transparency and repeatability of assessments in areas of risk analysis with limited data available. In this field, human reliability analysis (HRA) is no exception, and, in particular, dependence analysis is an HRA task strongly based on analyst judgement. The analysis of dependence among Human Failure Events refers to the assessment of the effect of an earlier human failure on the probability of the subsequent ones. This paper analyses and compares two expert systems, based on Bayesian Belief Networks and Fuzzy Logic (a Fuzzy Expert System, FES), respectively. The comparison shows that a BBN approach should be preferred in all the cases characterized by quantifiable uncertainty in the input (i.e. when probability distributions can be assigned to describe the input parameters uncertainty), since it provides a satisfactory representation of the uncertainty and its output is directly interpretable for use within PSA. On the other hand, in cases characterized by very limited knowledge, an analyst may feel constrained by the probabilistic framework, which requires assigning probability distributions for describing uncertainty. In these cases, the FES seems to lead to a more transparent representation of the input and output uncertainty. - Highlights: • We analyse treatment of uncertainty in two expert systems. • We compare a Bayesian Belief Network (BBN) and a Fuzzy Expert System (FES). • We focus on the input assessment, inference engines and output assessment. • We focus on an application problem of interest for human reliability analysis. • We emphasize the application rather than math to reach non-BBN or FES specialists

  4. Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel-Induced Pipeline Damage.

    Science.gov (United States)

    Zhang, Limao; Wu, Xianguo; Qin, Yawei; Skibniewski, Miroslaw J; Liu, Wenli

    2016-02-01

    Tunneling excavation is bound to produce significant disturbances to surrounding environments, and the tunnel-induced damage to adjacent underground buried pipelines is of considerable importance for geotechnical practice. A fuzzy Bayesian networks (FBNs) based approach for safety risk analysis is developed in this article with detailed step-by-step procedures, consisting of risk mechanism analysis, the FBN model establishment, fuzzification, FBN-based inference, defuzzification, and decision making. In accordance with the failure mechanism analysis, a tunnel-induced pipeline damage model is proposed to reveal the cause-effect relationships between the pipeline damage and its influential variables. In terms of the fuzzification process, an expert confidence indicator is proposed to reveal the reliability of the data when determining the fuzzy probability of occurrence of basic events, with both the judgment ability level and the subjectivity reliability level taken into account. By means of the fuzzy Bayesian inference, the approach proposed in this article is capable of calculating the probability distribution of potential safety risks and identifying the most likely potential causes of accidents under both prior knowledge and given evidence circumstances. A case concerning the safety analysis of underground buried pipelines adjacent to the construction of the Wuhan Yangtze River Tunnel is presented. The results demonstrate the feasibility of the proposed FBN approach and its application potential. The proposed approach can be used as a decision tool to provide support for safety assurance and management in tunnel construction, and thus increase the likelihood of a successful project in a complex project environment. © 2015 Society for Risk Analysis.

  5. Topics in Bayesian statistics and maximum entropy

    International Nuclear Information System (INIS)

    Mutihac, R.; Cicuttin, A.; Cerdeira, A.; Stanciulescu, C.

    1998-12-01

    Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)

  6. A new African fossil caprin and a combined molecular and morphological Bayesian phylogenetic analysis of caprini (Mammalia: Bovidae).

    Science.gov (United States)

    Bibi, F; Vrba, E; Fack, F

    2012-09-01

    Given that most species that have ever existed on Earth are extinct, no evolutionary history can ever be complete without the inclusion of fossil taxa. Bovids (antelopes and relatives) are one of the most diverse clades of large mammals alive today, with over a hundred living species and hundreds of documented fossil species. With the advent of molecular phylogenetics, major advances have been made in the phylogeny of this clade; however, there has been little attempt to integrate the fossil record into the developing phylogenetic picture. We here describe a new large fossil caprin species from ca. 1.9-Ma deposits from the Middle Awash, Ethiopia. To place the new species phylogenetically, we perform a Bayesian analysis of a combined molecular (cytochrome b) and morphological (osteological) character supermatrix. We include all living species of Caprini, the new fossil species, a fossil takin from the Pliocene of Ethiopia (Budorcas churcheri), and the insular subfossil Myotragus balearicus. The combined analysis demonstrates successful incorporation of both living and fossil species within a single phylogeny based on both molecular and morphological evidence. Analysis of the combined supermatrix produces superior resolution than with either the molecular or morphological data sets considered alone. Parsimony and Bayesian analyses of the data set are also compared and shown to produce similar results. The combined phylogenetic analysis indicates that the new fossil species is nested within Capra, making it one of the earliest representatives of this clade, with implications for molecular clock calibration. Geographical optimization indicates no less than four independent dispersals into Africa by caprins since the Pliocene. © 2012 The Authors. Journal of Evolutionary Biology © 2012 European Society For Evolutionary Biology.

  7. Training image analysis for model error assessment and dimension reduction in Bayesian-MCMC solutions to inverse problems

    Science.gov (United States)

    Koepke, C.; Irving, J.

    2015-12-01

    Bayesian solutions to inverse problems in near-surface geophysics and hydrology have gained increasing popularity as a means of estimating not only subsurface model parameters, but also their corresponding uncertainties that can be used in probabilistic forecasting and risk analysis. In particular, Markov-chain-Monte-Carlo (MCMC) methods have attracted much recent attention as a means of statistically sampling from the Bayesian posterior distribution. In this regard, two approaches are commonly used to improve the computational tractability of the Bayesian-MCMC approach: (i) Forward models involving a simplification of the underlying physics are employed, which offer a significant reduction in the time required to calculate data, but generally at the expense of model accuracy, and (ii) the model parameter space is represented using a limited set of spatially correlated basis functions as opposed to a more intuitive high-dimensional pixel-based parameterization. It has become well understood that model inaccuracies resulting from (i) can lead to posterior parameter distributions that are highly biased and overly confident. Further, when performing model reduction as described in (ii), it is not clear how the prior distribution for the basis weights should be defined because simple (e.g., Gaussian or uniform) priors that may be suitable for a pixel-based parameterization may result in a strong prior bias when used for the weights. To address the issue of model error resulting from known forward model approximations, we generate a set of error training realizations and analyze them with principal component analysis (PCA) in order to generate a sparse basis. The latter is used in the MCMC inversion to remove the main model-error component from the residuals. To improve issues related to prior bias when performing model reduction, we also use a training realization approach, but this time models are simulated from the prior distribution and analyzed using independent

  8. Bayesian Model Development for Analysis of Open Source Information to Support the Assessment of Nuclear Programs

    Energy Technology Data Exchange (ETDEWEB)

    Gastelum, Zoe N.; Whitney, Paul D.; White, Amanda M.

    2013-07-15

    Pacific Northwest National Laboratory has spent several years researching, developing, and validating large Bayesian network models to support integration of open source data sets for nuclear proliferation research. Our current work focuses on generating a set of interrelated models for multi-source assessment of nuclear programs, as opposed to a single comprehensive model. By using this approach, we can break down the models to cover logical sub-problems that can utilize different expertise and data sources. This approach allows researchers to utilize the models individually or in combination to detect and characterize a nuclear program and identify data gaps. The models operate at various levels of granularity, covering a combination of state-level assessments with more detailed models of site or facility characteristics. This paper will describe the current open source-driven, nuclear nonproliferation models under development, the pros and cons of the analytical approach, and areas for additional research.

  9. Computation of posterior distribution in Bayesian analysis – application in an intermittently used reliability system

    Directory of Open Access Journals (Sweden)

    V. S.S. Yadavalli

    2002-09-01

    Full Text Available Bayesian estimation is presented for the stationary rate of disappointments, D∞, for two models (with different specifications of intermittently used systems. The random variables in the system are considered to be independently exponentially distributed. Jeffreys’ prior is assumed for the unknown parameters in the system. Inference about D∞ is being restrained in both models by the complex and non-linear definition of D∞. Monte Carlo simulation is used to derive the posterior distribution of D∞ and subsequently the highest posterior density (HPD intervals. A numerical example where Bayes estimates and the HPD intervals are determined illustrates these results. This illustration is extended to determine the frequentistical properties of this Bayes procedure, by calculating covering proportions for each of these HPD intervals, assuming fixed values for the parameters.

  10. Bayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem.

    Science.gov (United States)

    Lawson, Daniel J; Holtrop, Grietje; Flint, Harry

    2011-07-01

    Process models specified by non-linear dynamic differential equations contain many parameters, which often must be inferred from a limited amount of data. We discuss a hierarchical Bayesian approach combining data from multiple related experiments in a meaningful way, which permits more powerful inference than treating each experiment as independent. The approach is illustrated with a simulation study and example data from experiments replicating the aspects of the human gut microbial ecosystem. A predictive model is obtained that contains prediction uncertainty caused by uncertainty in the parameters, and we extend the model to capture situations of interest that cannot easily be studied experimentally. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Insurance penetration and economic growth in Africa: Dynamic effects analysis using Bayesian TVP-VAR approach

    Directory of Open Access Journals (Sweden)

    D.O. Olayungbo

    2016-12-01

    Full Text Available This paper examines the dynamic interactions between insurance and economic growth in eight African countries for the period of 1970–2013. Insurance demand is measured by insurance penetration which accounts for income differences across the sample countries. A Bayesian Time Varying Parameter Vector Auto regression (TVP-VAR model with stochastic volatility is used to analyze the short run and the long run among the variables of interest. Using insurance penetration as a measure of insurance to economic growth, we find positive relationship for Egypt, while short-run negative and long-run positive effects are found for Kenya, Mauritius, and South Africa. On the contrary, negative effects are found for Algeria, Nigeria, Tunisia, and Zimbabwe. Implementation of sound financial reforms and wide insurance coverage are proposed recommendations for insurance development in the selected African countries.

  12. A Bayesian Analysis of a Random Effects Small Business Loan Credit Scoring Model

    Directory of Open Access Journals (Sweden)

    Patrick J. Farrell

    2011-09-01

    Full Text Available One of the most important aspects of credit scoring is constructing a model that has low misclassification rates and is also flexible enough to allow for random variation. It is also well known that, when there are a large number of highly correlated variables as is typical in studies involving questionnaire data, a method must be found to reduce the number of variables to those that have high predictive power. Here we propose a Bayesian multivariate logistic regression model with both fixed and random effects for small business loan credit scoring and a variable reduction method using Bayes factors. The method is illustrated on an interesting data set based on questionnaires sent to loan officers in Canadian banks and venture capital companies

  13. Analysis of housing price by means of STAR models with neighbourhood effects: a Bayesian approach

    Science.gov (United States)

    Beamonte, Asuncion; Gargallo, Pilar; Salvador, Manuel

    2010-06-01

    In this paper, we extend the Bayesian methodology introduced by Beamonte et al. (Stat Modelling 8:285-311, 2008) for the estimation and comparison of spatio-temporal autoregressive models (STAR) with neighbourhood effects, providing a more general treatment that uses larger and denser nets for the number of spatial and temporal influential neighbours and continuous distributions for their smoothing weights. This new treatment also reduces the computational time and the RAM necessities of the estimation algorithm in Beamonte et al. (Stat Modelling 8:285-311, 2008). The procedure is illustrated by an application to the Zaragoza (Spain) real estate market, improving the goodness of fit and the outsampling behaviour of the model thanks to a more flexible estimation of the neighbourhood parameters.

  14. Climate-informed flood frequency analysis based on Bayesian theory and teleconnection for the Three Gorges Dam (TGD)

    Science.gov (United States)

    DONG, Q.; Zhang, X.; Lall, U.; Sang, Y. F.; Xie, P.

    2017-12-01

    With the current global climate changing and human activities intensifying, the uncertainties and danger of floods increased significantly. However, the current flood frequency analysis is still based on the stationary assumption. This assumption not only limits the benefits of the water conservancy projects, but also brings hazard because it ignores the risk of flooding under climate change. In this paper, we relax the stationary hypothesis in the flood frequency analysis model based on the teleconnection and use the intrinsic relation of flood elements to improve the annual flood frequency results by Bayesian inference approaches. Daily discharges of the the Three Gorges Dam(TGD) in 1953-2013 years are used as an example. Firstly, according to the linear correlation between the climate indices and the distribution parameters, the prior distributions of peak and volume are established with the selected large scale climate predictors. After that, by using the copula function and predictands, the conditional probability function of peak and volume is obtained. Then, the Bayesian theory links the prior distributions and conditional distributions and get the posterior distributions. We compare the difference under different prior distributions and find the optimal flood frequency distribution model. Finally, we discuss the impact of dynamic flood frequency analysis on the plan and management of hydraulic engineering. The results show that compared with the prior probability, the posterior probability considering the correlation of the flood elements is more accurate and the uncertainty is smaller. And the dynamic flood frequency model has a great impact on the management of the existing hydraulic engineering, which can improve the engineering operation benefit and reducing its flood risk, but it nearly didn't influence the plan of hydraulic engineering. The study of this paper is helpful to the dynamic flood risk management of TGD, and provide reference for the

  15. Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC).

    Science.gov (United States)

    Kwon, Deukwoo; Reis, Isildinha M

    2015-08-12

    When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). In the estimation of the standard deviation, our ABC method performs better than the other methods when data are generated from skewed or heavy-tailed distributions. The corresponding average relative error (ARE) approaches zero as sample size increases. In data generated from the normal distribution, our ABC performs well. However, the Wan et al. method is best for estimating standard deviation under normal distribution. In the estimation of the mean, our ABC method is best regardless of assumed distribution. ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95 % credible interval when Bayesian analysis has been employed.

  16. A computer program for uncertainty analysis integrating regression and Bayesian methods

    Science.gov (United States)

    Lu, Dan; Ye, Ming; Hill, Mary C.; Poeter, Eileen P.; Curtis, Gary

    2014-01-01

    This work develops a new functionality in UCODE_2014 to evaluate Bayesian credible intervals using the Markov Chain Monte Carlo (MCMC) method. The MCMC capability in UCODE_2014 is based on the FORTRAN version of the differential evolution adaptive Metropolis (DREAM) algorithm of Vrugt et al. (2009), which estimates the posterior probability density function of model parameters in high-dimensional and multimodal sampling problems. The UCODE MCMC capability provides eleven prior probability distributions and three ways to initialize the sampling process. It evaluates parametric and predictive uncertainties and it has parallel computing capability based on multiple chains to accelerate the sampling process. This paper tests and demonstrates the MCMC capability using a 10-dimensional multimodal mathematical function, a 100-dimensional Gaussian function, and a groundwater reactive transport model. The use of the MCMC capability is made straightforward and flexible by adopting the JUPITER API protocol. With the new MCMC capability, UCODE_2014 can be used to calculate three types of uncertainty intervals, which all can account for prior information: (1) linear confidence intervals which require linearity and Gaussian error assumptions and typically 10s–100s of highly parallelizable model runs after optimization, (2) nonlinear confidence intervals which require a smooth objective function surface and Gaussian observation error assumptions and typically 100s–1,000s of partially parallelizable model runs after optimization, and (3) MCMC Bayesian credible intervals which require few assumptions and commonly 10,000s–100,000s or more partially parallelizable model runs. Ready access allows users to select methods best suited to their work, and to compare methods in many circumstances.

  17. A new software for deformation source optimization, the Bayesian Earthquake Analysis Tool (BEAT)

    Science.gov (United States)

    Vasyura-Bathke, H.; Dutta, R.; Jonsson, S.; Mai, P. M.

    2017-12-01

    Modern studies of crustal deformation and the related source estimation, including magmatic and tectonic sources, increasingly use non-linear optimization strategies to estimate geometric and/or kinematic source parameters and often consider both jointly, geodetic and seismic data. Bayesian inference is increasingly being used for estimating posterior distributions of deformation source model parameters, given measured/estimated/assumed data and model uncertainties. For instance, some studies consider uncertainties of a layered medium and propagate these into source parameter uncertainties, while others use informative priors to reduce the model parameter space. In addition, innovative sampling algorithms have been developed to efficiently explore the high-dimensional parameter spaces. Compared to earlier studies, these improvements have resulted in overall more robust source model parameter estimates that include uncertainties. However, the computational burden of these methods is high and estimation codes are rarely made available along with the published results. Even if the codes are accessible, it is usually challenging to assemble them into a single optimization framework as they are typically coded in different programing languages. Therefore, further progress and future applications of these methods/codes are hampered, while reproducibility and validation of results has become essentially impossible. In the spirit of providing open-access and modular codes to facilitate progress and reproducible research in deformation source estimations, we undertook the effort of developing BEAT, a python package that comprises all the above-mentioned features in one single programing environment. The package builds on the pyrocko seismological toolbox (www.pyrocko.org), and uses the pymc3 module for Bayesian statistical model fitting. BEAT is an open-source package (https://github.com/hvasbath/beat), and we encourage and solicit contributions to the project. Here, we

  18. Discriminant analysis in the presence of interferences: combined application of target factor analysis and a Bayesian soft-classifier.

    Science.gov (United States)

    Rinke, Caitlin N; Williams, Mary R; Brown, Christopher; Baudelet, Matthieu; Richardson, Martin; Sigman, Michael E

    2012-11-13

    A method is described for performing discriminant analysis in the presence of interfering background signal. The method is based on performing target factor analysis on a data set comprised of contributions from analyte(s) and interfering components. A library of data from representative analyte classes is tested for possible contributing factors by performing oblique rotations of the principal factors to obtain the best match, in a least-squares sense, between test and predicted vectors. The degree of match between the test and predicted vectors is measured by the Pearson correlation coefficient, r, and the distribution of r for each class is determined. A Bayesian soft classifier is used to calculate the posterior probability based on the distributions of r for each class, which assist the analyst in assessing the presence of one or more analytes. The method is demonstrated by analyses performed on spectra obtained by laser induced breakdown spectroscopy (LIBS). Single and multiple bullet jacketing transfers to steel and porcelain substrates were analyzed to identify the jacketing materials. Additionally, the metal surrounding bullet holes was analyzed to identify the class of bullet jacketing that passed through a stainless steel plate. Of 36 single sample transfers, the copper jacketed (CJ) and non-jacketed (NJ) class on porcelain had an average posterior probability of the metal deposited on the substrate of 1.0. Metal jacketed (MJ) bullet transfers to steel and porcelain were not detected as successfully. Multiple transfers of CJ/NJ and CJ/MJ on the two substrates resulted in posterior probabilities that reflected the presence of both jacketing materials. The MJ/NJ transfers gave posterior probabilities that reflected the presence of the NJ material, but the MJ component was mistaken for CJ on steel, while non-zero probabilities were obtained for both CJ and MJ on porcelain. Jacketing transfer from a bullet to steel as the projectile passed through the steel

  19. Bayesian Nonparametric Hidden Markov Models with application to the analysis of copy-number-variation in mammalian genomes.

    Science.gov (United States)

    Yau, C; Papaspiliopoulos, O; Roberts, G O; Holmes, C

    2011-01-01

    We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models. Our approach uses a Mixture of Dirichlet Process (MDP) model for the unknown sampling distribution (likelihood) for the observations arising in each state and a computationally efficient data augmentation scheme to aid inference. The method uses novel MCMC methodology which combines recent retrospective sampling methods with the use of slice sampler variables. The methodology is computationally efficient, both in terms of MCMC mixing properties, and robustness to the length of the time series being investigated. Moreover, the method is easy to implement requiring little or no user-interaction. We apply our methodology to the analysis of genomic copy number variation.

  20. On-the-fly analysis of molecular dynamics simulation trajectories of proteins using the Bayesian inference method

    Science.gov (United States)

    Miyashita, Naoyuki; Yonezawa, Yasushige

    2017-09-01

    Robust and reliable analyses of long trajectories from molecular dynamics simulations are important for investigations of functions and mechanisms of proteins. Structural fitting is necessary for various analyses of protein dynamics, thus removing time-dependent translational and rotational movements. However, the fitting is often difficult for highly flexible molecules. Thus, to address the issues, we proposed a fitting algorithm that uses the Bayesian inference method in combination with rotational fitting-weight improvements, and the well-studied globular protein systems trpcage and lysozyme were used for investigations. The present method clearly identified rigid core regions that fluctuate less than other regions and also separated core regions from highly fluctuating regions with greater accuracy than conventional methods. Our method also provided simultaneous variance-covariance matrix elements composed of atomic coordinates, allowing us to perform principle component analysis and prepare domain cross-correlation map during molecular dynamics simulations in an on-the-fly manner.

  1. Bayesian programming

    CERN Document Server

    Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel

    2013-01-01

    Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean

  2. Bayesian methods for measures of agreement

    CERN Document Server

    Broemeling, Lyle D

    2009-01-01

    Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software.Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation...

  3. Linking bovine tuberculosis on cattle farms to white-tailed deer and environmental variables using Bayesian hierarchical analysis.

    Directory of Open Access Journals (Sweden)

    W David Walter

    Full Text Available Bovine tuberculosis is a bacterial disease caused by Mycobacterium bovis in livestock and wildlife with hosts that include Eurasian badgers (Meles meles, brushtail possum (Trichosurus vulpecula, and white-tailed deer (Odocoileus virginianus. Risk-assessment efforts in Michigan have been initiated on farms to minimize interactions of cattle with wildlife hosts but research on M. bovis on cattle farms has not investigated the spatial context of disease epidemiology. To incorporate spatially explicit data, initial likelihood of infection probabilities for cattle farms tested for M. bovis, prevalence of M. bovis in white-tailed deer, deer density, and environmental variables for each farm were modeled in a Bayesian hierarchical framework. We used geo-referenced locations of 762 cattle farms that have been tested for M. bovis, white-tailed deer prevalence, and several environmental variables that may lead to long-term survival and viability of M. bovis on farms and surrounding habitats (i.e., soil type, habitat type. Bayesian hierarchical analyses identified deer prevalence and proportion of sandy soil within our sampling grid as the most supported model. Analysis of cattle farms tested for M. bovis identified that for every 1% increase in sandy soil resulted in an increase in odds of infection by 4%. Our analysis revealed that the influence of prevalence of M. bovis in white-tailed deer was still a concern even after considerable efforts to prevent cattle interactions with white-tailed deer through on-farm mitigation and reduction in the deer population. Cattle farms test positive for M. bovis annually in our study area suggesting that the potential for an environmental source either on farms or in the surrounding landscape may contributing to new or re-infections with M. bovis. Our research provides an initial assessment of potential environmental factors that could be incorporated into additional modeling efforts as more knowledge of deer herd

  4. Using a Simple Binomial Model to Assess Improvement in Predictive Capability: Sequential Bayesian Inference, Hypothesis Testing, and Power Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Sigeti, David E. [Los Alamos National Laboratory; Pelak, Robert A. [Los Alamos National Laboratory

    2012-09-11

    We present a Bayesian statistical methodology for identifying improvement in predictive simulations, including an analysis of the number of (presumably expensive) simulations that will need to be made in order to establish with a given level of confidence that an improvement has been observed. Our analysis assumes the ability to predict (or postdict) the same experiments with legacy and new simulation codes and uses a simple binomial model for the probability, {theta}, that, in an experiment chosen at random, the new code will provide a better prediction than the old. This model makes it possible to do statistical analysis with an absolute minimum of assumptions about the statistics of the quantities involved, at the price of discarding some potentially important information in the data. In particular, the analysis depends only on whether or not the new code predicts better than the old in any given experiment, and not on the magnitude of the improvement. We show how the posterior distribution for {theta} may be used, in a kind of Bayesian hypothesis testing, both to decide if an improvement has been observed and to quantify our confidence in that decision. We quantify the predictive probability that should be assigned, prior to taking any data, to the possibility of achieving a given level of confidence, as a function of sample size. We show how this predictive probability depends on the true value of {theta} and, in particular, how there will always be a region around {theta} = 1/2 where it is highly improbable that we will be able to identify an improvement in predictive capability, although the width of this region will shrink to zero as the sample size goes to infinity. We show how the posterior standard deviation may be used, as a kind of 'plan B metric' in the case that the analysis shows that {theta} is close to 1/2 and argue that such a plan B should generally be part of hypothesis testing. All the analysis presented in the paper is done with a

  5. Making Sense of a Negative Clinical Trial Result: A Bayesian Analysis of a Clinical Trial of Lorazepam and Diazepam for Pediatric Status Epilepticus.

    Science.gov (United States)

    Chamberlain, Daniel B; Chamberlain, James M

    2017-01-01

    We demonstrate the application of a Bayesian approach to a recent negative clinical trial result. A Bayesian analysis of such a trial can provide a more useful interpretation of results and can incorporate previous evidence. This was a secondary analysis of the efficacy and safety results of the Pediatric Seizure Study, a randomized clinical trial of lorazepam versus diazepam for pediatric status epilepticus. We included the published results from the only prospective pediatric study of status in a Bayesian hierarchic model, and we performed sensitivity analyses on the amount of pooling between studies. We evaluated 3 summary analyses for the results: superiority, noninferiority (margin diazepam. There is a 95% probability that the true efficacy of lorazepam is in the range of 66% to 80%. For both the efficacy and safety outcomes, there was greater than 95% probability that lorazepam is noninferior to diazepam, and there was greater than 90% probability that the 2 medications are practically equivalent. The results were largely driven by the current study because of the sample sizes of our study (n=273) and the previous pediatric study (n=61). Because Bayesian analysis estimates the probability of one or more hypotheses, such an approach can provide more useful information about the meaning of the results of a negative trial outcome. In the case of pediatric status epilepticus, it is highly likely that lorazepam is noninferior and practically equivalent to diazepam. Copyright © 2016 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

  6. Bayesian coronal seismology

    Science.gov (United States)

    Arregui, Iñigo

    2018-01-01

    In contrast to the situation in a laboratory, the study of the solar atmosphere has to be pursued without direct access to the physical conditions of interest. Information is therefore incomplete and uncertain and inference methods need to be employed to diagnose the physical conditions and processes. One of such methods, solar atmospheric seismology, makes use of observed and theoretically predicted properties of waves to infer plasma and magnetic field properties. A recent development in solar atmospheric seismology consists in the use of inversion and model comparison methods based on Bayesian analysis. In this paper, the philosophy and methodology of Bayesian analysis are first explained. Then, we provide an account of what has been achieved so far from the application of these techniques to solar atmospheric seismology and a prospect of possible future extensions.

  7. Using mixture of Gamma distributions for Bayesian analysis in an M/G/1 queue with optional second service

    NARCIS (Netherlands)

    Mohammadi, A.; Salehi-Rad, M. R.; Wit, E. C.

    The paper proposes Bayesian framework in an M/G/1 queuing system with optional second service. The semi-parametric model based on a finite mixture of Gamma distributions is considered to approximate both the general service and re-service times densities in this queuing system. A Bayesian procedure

  8. Bayesian networks in reliability

    Energy Technology Data Exchange (ETDEWEB)

    Langseth, Helge [Department of Mathematical Sciences, Norwegian University of Science and Technology, N-7491 Trondheim (Norway)]. E-mail: helgel@math.ntnu.no; Portinale, Luigi [Department of Computer Science, University of Eastern Piedmont ' Amedeo Avogadro' , 15100 Alessandria (Italy)]. E-mail: portinal@di.unipmn.it

    2007-01-15

    Over the last decade, Bayesian networks (BNs) have become a popular tool for modelling many kinds of statistical problems. We have also seen a growing interest for using BNs in the reliability analysis community. In this paper we will discuss the properties of the modelling framework that make BNs particularly well suited for reliability applications, and point to ongoing research that is relevant for practitioners in reliability.

  9. Bayesian Analysis of Hot Jupiter Radius Anomalies Points to Ohmic Dissipation

    Science.gov (United States)

    Thorngren, Daniel; Fortney, Jonathan

    2018-01-01

    The cause of the unexpectedly large radii of hot Jupiters has been the subject of many hypotheses over the past 15 years and is one of the long-standing open issues in exoplanetary physics. In our work, we seek to examine the population of 300 hot Jupiters to identify a model that best explains their radii. Using a hierarchical Bayesian framework, we match structure evolution models to the observed giant planets’ masses, radii, and ages, with a prior for bulk composition based on the mass from Thorngren et al. (2016). We consider various models for the relationship between heating efficiency (the fraction of flux absorbed into the interior) and incident flux. For the first time, we are able to derive this heating efficiency as a function of planetary T_eq. Models in which the heating efficiency decreases at the higher temperatures (above ~1600 K) are strongly and statistically significantly preferred. Of the published models for the radius anomaly, only the Ohmic dissipation model predicts this feature, which it explains as being the result of magnetic drag reducing atmospheric wind speeds. We interpret our results as evidence in favor of the Ohmic dissipation model.

  10. Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach.

    Science.gov (United States)

    Mohammadi, Tayeb; Kheiri, Soleiman; Sedehi, Morteza

    2016-01-01

    Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables "number of blood donation" and "number of blood deferral": as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models.

  11. Inferring the physical properties of yeast chromatin through Bayesian analysis of whole nucleus simulations.

    Science.gov (United States)

    Arbona, Jean-Michel; Herbert, Sébastien; Fabre, Emmanuelle; Zimmer, Christophe

    2017-05-03

    The structure and mechanical properties of chromatin impact DNA functions and nuclear architecture but remain poorly understood. In budding yeast, a simple polymer model with minimal sequence-specific constraints and a small number of structural parameters can explain diverse experimental data on nuclear architecture. However, how assumed chromatin properties affect model predictions was not previously systematically investigated. We used hundreds of dynamic chromosome simulations and Bayesian inference to determine chromatin properties consistent with an extensive dataset that includes hundreds of measurements from imaging in fixed and live cells and two Hi-C studies. We place new constraints on average chromatin fiber properties, narrowing down the chromatin compaction to ~53-65 bp/nm and persistence length to ~52-85 nm. These constraints argue against a 20-30 nm fiber as the exclusive chromatin structure in the genome. Our best model provides a much better match to experimental measurements of nuclear architecture and also recapitulates chromatin dynamics measured on multiple loci over long timescales. This work substantially improves our understanding of yeast chromatin mechanics and chromosome architecture and provides a new analytic framework to infer chromosome properties in other organisms.

  12. Bayesian analysis of overdispersed chromosome aberration data with the negative binomial model

    International Nuclear Information System (INIS)

    Brame, R.S.; Groer, P.G.

    2002-01-01

    The usual assumption of a Poisson model for the number of chromosome aberrations in controlled calibration experiments implies variance equal to the mean. However, it is known that chromosome aberration data from experiments involving high linear energy transfer radiations can be overdispersed, i.e. the variance is greater than the mean. Present methods for dealing with overdispersed chromosome data rely on frequentist statistical techniques. In this paper, the problem of overdispersion is considered from a Bayesian standpoint. The Bayes Factor is used to compare Poisson and negative binomial models for two previously published calibration data sets describing the induction of dicentric chromosome aberrations by high doses of neutrons. Posterior densities for the model parameters, which characterise dose response and overdispersion are calculated and graphed. Calibrative densities are derived for unknown neutron doses from hypothetical radiation accident data to determine the impact of different model assumptions on dose estimates. The main conclusion is that an initial assumption of a negative binomial model is the conservative approach to chromosome dosimetry for high LET radiations. (author)

  13. Bayesian spatio-temporal analysis and geospatial risk factors of human monocytic ehrlichiosis.

    Directory of Open Access Journals (Sweden)

    Ram K Raghavan

    Full Text Available Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005-2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER], and socio-economic conditions (US Census Bureau were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005-2012, and identified poverty status, relative humidity, and an interactive factor, 'diurnal temperature range x mixed forest area' as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases.

  14. Bayesian Analysis of Systematic Effects in Interferometric Observations of the Cosmic Microwave Background Polarization

    Science.gov (United States)

    Karakci, Ata; Zhang, L.; Sutter, P. M.; Bunn, E. F.; Korotkov, A.; Timbie, P. T.; Tucker, G. S.; Wandelt, B.

    2013-06-01

    The detection of the primordial B-mode spectrum of the polarized cosmic microwave background (CMB) signal may provide a probe of inflation. However, observation of such a faint signal requires excellent control of systematic errors. Interferometry proves to be a promising approach for overcoming such a challenge. In this thesis we present a complete simulation pipeline of interferometric observations of CMB polarization, including systematic errors. We employ a method for Bayesian inference of power spectra and signal reconstruction from interferometric data of the CMB polarization signal by using the technique of Gibbs sampling. Several categories of systematic errors are considered: instrumental errors, consisting of antenna gain and antenna coupling errors, and beam errors, consisting of antenna pointing errors, beam cross-polarization and beam shape (and size) errors. In order to recover the tensor-to-scalar ratio, r, within a 10% tolerance level, which ensures the experiment is sensitive enough to detect the B-signal at r=0.01 in the multipole range 28 QUBIC-like experiment, Gaussian-distributed systematic errors must be controlled with precisions of |g_rms| = 0.1 for antenna gain, |e_rms| = 5e-4 for antenna coupling, d_rms ~ 0.7 degrees for pointing, z_rms ~ 0.7 degrees for beam shape, and m_rms = 5e-4 for beam cross-polarization.

  15. Recent misconceptions about the 'database search problem': a probabilistic analysis using Bayesian networks.

    Science.gov (United States)

    Biedermann, A; Gittelson, S; Taroni, F

    2011-10-10

    This paper analyses and discusses arguments that emerge from a recent discussion about the proper assessment of the evidential value of correspondences observed between the characteristics of a crime stain and those of a sample from a suspect when (i) this latter individual is found as a result of a database search and (ii) remaining database members are excluded as potential sources (because of different analytical characteristics). Using a graphical probability approach (i.e., Bayesian networks), the paper here intends to clarify that there is no need to (i) introduce a correction factor equal to the size of the searched database (i.e., to reduce a likelihood ratio), nor to (ii) adopt a propositional level not directly related to the suspect matching the crime stain (i.e., a proposition of the kind 'some person in (outside) the database is the source of the crime stain' rather than 'the suspect (some other person) is the source of the crime stain'). The present research thus confirms existing literature on the topic that has repeatedly demonstrated that the latter two requirements (i) and (ii) should not be a cause of concern. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  16. Team performance and collective efficacy in the dynamic psychology of competitive team: a Bayesian network analysis.

    Science.gov (United States)

    Fuster-Parra, P; García-Mas, A; Ponseti, F J; Leo, F M

    2015-04-01

    The purpose of this paper was to discover the relationships among 22 relevant psychological features in semi-professional football players in order to study team's performance and collective efficacy via a Bayesian network (BN). The paper includes optimization of team's performance and collective efficacy using intercausal reasoning pattern which constitutes a very common pattern in human reasoning. The BN is used to make inferences regarding our problem, and therefore we obtain some conclusions; among them: maximizing the team's performance causes a decrease in collective efficacy and when team's performance achieves the minimum value it causes an increase in moderate/high values of collective efficacy. Similarly, we may reason optimizing team collective efficacy instead. It also allows us to determine the features that have the strongest influence on performance and which on collective efficacy. From the BN two different coaching styles were differentiated taking into account the local Markov property: training leadership and autocratic leadership. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Bayesian analysis of growth curves using mixed models defined by stochastic differential equations.

    Science.gov (United States)

    Donnet, Sophie; Foulley, Jean-Louis; Samson, Adeline

    2010-09-01

    Growth curve data consist of repeated measurements of a continuous growth process over time in a population of individuals. These data are classically analyzed by nonlinear mixed models. However, the standard growth functions used in this context prescribe monotone increasing growth and can fail to model unexpected changes in growth rates. We propose to model these variations using stochastic differential equations (SDEs) that are deduced from the standard deterministic growth function by adding random variations to the growth dynamics. A Bayesian inference of the parameters of these SDE mixed models is developed. In the case when the SDE has an explicit solution, we describe an easily implemented Gibbs algorithm. When the conditional distribution of the diffusion process has no explicit form, we propose to approximate it using the Euler-Maruyama scheme. Finally, we suggest validating the SDE approach via criteria based on the predictive posterior distribution. We illustrate the efficiency of our method using the Gompertz function to model data on chicken growth, the modeling being improved by the SDE approach. © 2009 INRA, Government of France.

  18. Validity and usefulness of the Line Drill test for adolescent basketball players: a Bayesian multilevel analysis.

    Science.gov (United States)

    Carvalho, Humberto M; Gonçalves, Carlos E; Grosgeorge, Bernard; Paes, Roberto R

    2017-01-01

    The study examined the validity of the Line Drill test (LD) in male adolescent basketball players (10-15 years). Sensitiveness of the LD to changes in performance across a training and competition season (4 months) was also considered. Age, maturation, body size and LD were measured (n = 57). Sensitiveness of the LD was examined pre- and post-competitive season in a sub-sample (n = 44). The time at each of the four shuttle sprints of the LD (i.e. four stages) was modelled with Bayesian multilevel models. We observed very large correlation of performance at stage 4 (full LD protocol) with stage 3, but lower correlations with the early LD stages. Players' performance by somatic maturity differed substantially only when considering full LD protocol performance. Substantial improvements in all stages of the protocol were observed across the 4-month competitive season. The LD protocol should be shortened by the last full court shuttle sprint, remaining sensitive to training exposure, and independent of maturity status and body size.

  19. Bayesian analysis of culture and PCR methods for detection of Campylobacter spp. in broiler caecal samples.

    Science.gov (United States)

    Arnold, M E; Jones, E M; Lawes, J R; Vidal, A B; Clifton-Hadley, F A; Rodgers, J D; Powell, L F

    2015-01-01

    The objective of this study was to estimate the sensitivity and specificity of a culture method and a polymerase chain reaction (PCR) method for detection of two Campylobacter species: C. jejuni and C. coli. Data were collected during a 3-year survey of UK broiler flocks, and consisted of parallel sampling of caeca from 436 batches of birds by both PCR and culture. Batches were stratified by season (summer/non-summer) and whether they were the first depopulation of the flock, resulting in four sub-populations. A Bayesian approach in the absence of a gold standard was adopted, and the sensitivity and specificity of the PCR and culture for each Campylobacter subtype was estimated, along with the true C. jejuni and C. coli prevalence in each sub-population. Results indicated that the sensitivity of the culture method was higher than that of PCR in detecting both species when the samples were derived from populations infected with at most one species of Campylobacter. However, from a mixed population, the sensitivity of culture for detecting both C. jejuni or C. coli is reduced while PCR is potentially able to detect both species, although the total probability of correctly identifying at least one species by PCR is similar to that of the culture method.

  20. A Risk Analysis of the Molybdenum-99 Supply Chain Using Bayesian Networks

    Science.gov (United States)

    Liang, Jeffrey Ryan

    The production of Molybdenum-99 (99Mo) is critical to the field of nuclear medicine, where it is utilized in roughly 80% of all nuclear imaging procedures. In October of 2016, the National Research Universal (NRU) reactor in Canada, which historically had the highest 99Mo production capability worldwide, ceased routine production and will be permanently shut down in 2018. This loss of capacity has led to widespread concern over the ability of the 99Mo supply chain and to meet demand. There is significant disagreement among analyses from trade groups, governments, and other researchers, predicting everything from no significant impact to major worldwide shortages. Using Bayesian networks, this research focused on modeling the 99Mo supply chain to quantify how a disrupting event, such as the unscheduled downtime of a reactor, will impact the global supply. This not only includes quantifying the probability of a shortage occurring, but also identifying which nodes in the supply chain introduce the most risk to better inform decision makers on where future facilities or other risk mitigation techniques should be applied.

  1. Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks.

    Science.gov (United States)

    Zarringhalam, Kourosh; Enayetallah, Ahmed; Reddy, Padmalatha; Ziemek, Daniel

    2014-06-15

    Understanding and predicting an individual's response in a clinical trial is the key to better treatments and cost-: effective medicine. Over the coming years, more and more large-scale omics datasets will become available to characterize patients with complex and heterogeneous diseases at a molecular level. Unfortunately, genetic, phenotypical and environmental variation is much higher in a human trial population than currently modeled or measured in most animal studies. In our experience, this high variability can lead to failure of trained predictors in independent studies and undermines the credibility and utility of promising high-dimensional datasets. We propose a method that utilizes patient-level genome-wide expression data in conjunction with causal networks based on prior knowledge. Our approach determines a differential expression profile for each patient and uses a Bayesian approach to infer corresponding upstream regulators. These regulators and their corresponding posterior probabilities of activity are used in a regularized regression framework to predict response. We validated our approach using two clinically relevant phenotypes, namely acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. To demonstrate pitfalls in translating trained predictors across independent trials, we analyze performance characteristics of our approach as well as alternative feature sets in the regression on two independent datasets for each phenotype. We show that the proposed approach is able to successfully incorporate causal prior knowledge to give robust performance estimates. © The Author 2014. Published by Oxford University Press.

  2. Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis.

    Directory of Open Access Journals (Sweden)

    Zsolt Zador

    Full Text Available Traumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain further insight into predictor importance.We analyzed the corticosteroid randomization after significant head injury (CRASH trial database of 10008 patients and included patients for whom demographics, injury characteristics, computer tomography (CT findings and Glasgow Outcome Scale (GCS were recorded (total of 13 predictors, which would be available to clinicians within a few hours following the injury in 6945 patients. Predictions of clinical outcome (death or severe disability at 6 months were performed using logistic regression models with 5-fold cross validation. Predictive performance was measured using standardized partial area (pAUC under the receiver operating curve (ROC and we used Delong test for comparisons. Variable importance ranking was based on pAUC targeted at specificity (pAUCSP and sensitivity (pAUCSE intervals of 90-100%. Probabilistic associations were depicted using Bayesian networks.Complete AUC analysis showed very good predictive power (AUC = 0.8237, 95% CI: 0.8138-0.8336 for the complete model. Specificity focused importance ranking highlighted age, pupillary, motor responses, obliteration of basal cisterns/3rd ventricle and midline shift. Interestingly when targeting model sensitivity, the highest-ranking variables were age, severe extracranial injury, verbal response, hematoma on CT and motor response. Simplified models, which included only these key predictors, had similar performance (pAUCSP = 0.6523, 95% CI: 0

  3. Bayesian analysis for inference of an emerging epidemic: citrus canker in urban landscapes.

    Directory of Open Access Journals (Sweden)

    Franco M Neri

    2014-04-01

    Full Text Available Outbreaks of infectious diseases require a rapid response from policy makers. The choice of an adequate level of response relies upon available knowledge of the spatial and temporal parameters governing pathogen spread, affecting, amongst others, the predicted severity of the epidemic. Yet, when a new pathogen is introduced into an alien environment, such information is often lacking or of no use, and epidemiological parameters must be estimated from the first observations of the epidemic. This poses a challenge to epidemiologists: how quickly can the parameters of an emerging disease be estimated? How soon can the future progress of the epidemic be reliably predicted? We investigate these issues using a unique, spatially and temporally resolved dataset for the invasion of a plant disease, Asiatic citrus canker in urban Miami. We use epidemiological models, Bayesian Markov-chain Monte Carlo, and advanced spatial statistical methods to analyse rates and extent of spread of the disease. A rich and complex epidemic behaviour is revealed. The spatial scale of spread is approximately constant over time and can be estimated rapidly with great precision (although the evidence for long-range transmission is inconclusive. In contrast, the rate of infection is characterised by strong monthly fluctuations that we associate with extreme weather events. Uninformed predictions from the early stages of the epidemic, assuming complete ignorance of the future environmental drivers, fail because of the unpredictable variability of the infection rate. Conversely, predictions improve dramatically if we assume prior knowledge of either the main environmental trend, or the main environmental events. A contrast emerges between the high detail attained by modelling in the spatiotemporal description of the epidemic and the bottleneck imposed on epidemic prediction by the limits of meteorological predictability. We argue that identifying such bottlenecks will be a

  4. Maximum likelihood Bayesian model averaging and its predictive analysis for groundwater reactive transport models

    Science.gov (United States)

    Curtis, Gary P.; Lu, Dan; Ye, Ming

    2015-01-01

    While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the

  5. Logistic Bayesian LASSO for genetic association analysis of data from complex sampling designs.

    Science.gov (United States)

    Zhang, Yuan; Hofmann, Jonathan N; Purdue, Mark P; Lin, Shili; Biswas, Swati

    2017-09-01

    Detecting gene-environment interactions with rare variants is critical in dissecting the etiology of common diseases. Interactions with rare haplotype variants (rHTVs) are of particular interest. At the same time, complex sampling designs, such as stratified random sampling, are becoming increasingly popular for designing case-control studies, especially for recruiting controls. The US Kidney Cancer Study (KCS) is an example, wherein all available cases were included while the controls at each site were randomly selected from the population by frequency matching with cases based on age, sex and race. There is currently no rHTV association method that can account for such a complex sampling design. To fill this gap, we consider logistic Bayesian LASSO (LBL), an existing rHTV approach for case-control data, and show that its model can easily accommodate the complex sampling design. We study two extensions that include stratifying variables either as main effects only or with additional modeling of their interactions with haplotypes. We conduct extensive simulation studies to compare the complex sampling methods with the original LBL methods. We find that, when there is no interaction between haplotype and stratifying variables, both extensions perform well while the original LBL methods lead to inflated type I error rates. However, when such an interaction exists, it is necessary to include the interaction effect in the model to control the type I error rate. Finally, we analyze the KCS data and find a significant interaction between (current) smoking and a specific rHTV in the N-acetyltransferase 2 gene.

  6. Bayesian analysis of canopy transpiration models: A test of posterior parameter means against measurements

    Science.gov (United States)

    Mackay, D. Scott; Ewers, Brent E.; Loranty, Michael M.; Kruger, Eric L.; Samanta, Sudeep

    2012-04-01

    SummaryBig-leaf models of transpiration are based on the hypothesis that structural heterogeneity within forest canopies can be ignored at stand or larger scales. However, the adoption of big-leaf models is de facto rather than de jure, as forests are never structurally or functionally homogeneous. We tested big-leaf models both with and without modification to include canopy gaps, in a heterogeneous quaking aspen stand having a range of canopy densities. Leaf area index (L) and canopy closure were obtained from biometric data, stomatal conductance parameters were obtained from sap flux measurements, while leaf gas exchange data provided photosynthetic parameters. We then rigorously tested model-data consistency by incrementally starving the models of these measured parameters and using Bayesian Markov Chain Monte Carlo simulation to retrieve the withheld parameters. Model acceptability was quantified with Deviance Information Criterion (DIC), which penalized model accuracy by the number of retrieved parameters. Big-leaf models overestimated canopy transpiration with increasing error as canopy density declined, but models that included gaps had minimal error regardless of canopy density. When models used measured L the other parameters were retrieved with minimal bias. This showed that simple canopy models could predict transpiration in data scarce regions where only L was measured. Models that had L withheld had the lowest DIC values suggesting that they were the most acceptable models. However, these models failed to retrieve unbiased parameter estimates indicating a mismatch between model structure and data. By quantifying model structure and data requirements this new approach to evaluating model-data fusion has advanced the understanding of canopy transpiration.

  7. A Primer on Bayesian Decision Analysis With an Application to a Personalized Kidney Transplant Decision

    Science.gov (United States)

    Neapolitan, Richard; Jiang, Xia; Ladner, Daniela P.; Kaplan, Bruce

    2016-01-01

    To provide personalized medicine, we not only must determine the treatments and other decisions most likely to be effective for a patient, but also consider the patient’s tradeoff between possible benefits of therapy versus possible loss of quality of life. There are numerous studies indicating that various treatments can negatively affect quality of life. Even if we have all information available for a given patient, it is an arduous task to amass the information to reach a decision that maximizes the utility of the decision to the patient. A clinical decision support system (CDSS) is a computer program, which is designed to assist healthcare professionals with decision making tasks. By utilizing emerging large datasets, we hold promise for developing CDSSs that can predict how treatments and other decisions can affect outcomes. However, we need to go beyond that; namely our CDSS needs to account for the extent to which these decisions can affect quality of life. This manuscript provides an introduction to developing CDSSs using Bayesian networks and influence diagrams. Such CDSSs are able to recommend decisions that maximize the expected utility of the predicted outcomes to the patient. By way of comparison, we examine the benefit and challenges of the Kidney Donor Risk Index (KDRI) as a decision support tool, and we discuss several difficulties with this index. Most importantly, the KDRI does not provide a measure of the expected quality of life if the kidney is accepted versus the expected quality of life if the patient stays on dialysis. Finally, we develop a schema for an influence diagram that models the kidney transplant decision, and show how the influence diagram approach can resolve these difficulties and provide the clinician and the potential transplant recipient with a valuable decision support tool. PMID:26900809

  8. 12th Brazilian Meeting on Bayesian Statistics

    CERN Document Server

    Louzada, Francisco; Rifo, Laura; Stern, Julio; Lauretto, Marcelo

    2015-01-01

    Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesia...

  9. Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis.

    Directory of Open Access Journals (Sweden)

    Matthieu Vignes

    Full Text Available Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth "Dialogue for Reverse Engineering Assessments and Methods" (DREAM5 challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on "Systems Genetics" proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics.

  10. A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid identification of Bacillus spores and classification of Bacillus species.

    Science.gov (United States)

    Correa, Elon; Goodacre, Royston

    2011-01-26

    The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS) have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS. We develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers) to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features. This final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA). The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA.

  11. CRAFT (complete reduction to amplitude frequency table)--robust and time-efficient Bayesian approach for quantitative mixture analysis by NMR.

    Science.gov (United States)

    Krishnamurthy, Krish

    2013-12-01

    The intrinsic quantitative nature of NMR is increasingly exploited in areas ranging from complex mixture analysis (as in metabolomics and reaction monitoring) to quality assurance/control. Complex NMR spectra are more common than not, and therefore, extraction of quantitative information generally involves significant prior knowledge and/or operator interaction to characterize resonances of interest. Moreover, in most NMR-based metabolomic experiments, the signals from metabolites are normally present as a mixture of overlapping resonances, making quantification difficult. Time-domain Bayesian approaches have been reported to be better than conventional frequency-domain analysis at identifying subtle changes in signal amplitude. We discuss an approach that exploits Bayesian analysis to achieve a complete reduction to amplitude frequency table (CRAFT) in an automated and time-efficient fashion - thus converting the time-domain FID to a frequency-amplitude table. CRAFT uses a two-step approach to FID analysis. First, the FID is digitally filtered and downsampled to several sub FIDs, and secondly, these sub FIDs are then modeled as sums of decaying sinusoids using the Bayesian approach. CRAFT tables can be used for further data mining of quantitative information using fingerprint chemical shifts of compounds of interest and/or statistical analysis of modulation of chemical quantity in a biological study (metabolomics) or process study (reaction monitoring) or quality assurance/control. The basic principles behind this approach as well as results to evaluate the effectiveness of this approach in mixture analysis are presented. Copyright © 2013 John Wiley & Sons, Ltd.

  12. Socio-environmental drivers and suicide in Australia: Bayesian spatial analysis

    Science.gov (United States)

    2014-01-01

    Background The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods. Methods Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology. A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs. Results In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR)ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RRUNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RRATSI = 1.0126, 95% CI: 1.0076-1.0176; RRUNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RRSEIFA = 0.9983, 95% CI: 0.9971-0.9995; RRATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1°C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996–2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001–2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and

  13. Socio-environmental drivers and suicide in Australia: Bayesian spatial analysis.

    Science.gov (United States)

    Qi, Xin; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

    2014-07-04

    The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods. Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology. A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs. In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR)ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RRUNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RRATSI = 1.0126, 95% CI: 1.0076-1.0176; RRUNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RRSEIFA = 0.9983, 95% CI: 0.9971-0.9995; RRATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1°C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996-2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001-2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and suicide in Model III. Proportion of Aboriginal and Torres

  14. A Bayesian uncertainty analysis of cetacean demography and bycatch mortality using age-at-death data.

    Science.gov (United States)

    Moore, Jeffrey E; Read, Andrew J

    2008-12-01

    Wildlife ecologists and managers are challenged to make the most of sparse information for understanding demography of many species, especially those that are long lived and difficult to observe. For many odontocete (dolphin, porpoise, toothed whale) populations, only fertility and age-at-death data are feasibly obtainable. We describe a Bayesian approach for using fertilities and two types of age-at-death data (i.e., age structure of deaths from all mortality sources and age structure of anthropogenic mortalities only) to estimate rate of increase, mortality rates, and impacts of anthropogenic mortality on those rates for a population assumed to be in a stable age structure. We used strandings data from 1977 to 1993 (n = 96) and observer bycatch data from 1989 to 1993 (n = 233) for the Gulf of Maine, USA, and Bay of Fundy, Canada, harbor porpoise (Phocoena phocoena) population as a case study. Our method combines mortality risk functions to estimate parameters describing age-specific natural and bycatch mortality rates. Separate functions are simultaneously fit to bycatch and strandings data, the latter of which are described as a mixture of natural and bycatch mortalities. Euler-Lotka equations and an estimate of longevity were used to constrain parameter estimates, and we included a parameter to account for unequal probabilities of natural vs. bycatch deaths occurring in a sample. We fit models under two scenarios intended to correct for possible data bias due to indirect bycatch of calves (i.e., death following bycatch mortality of mothers) being underrepresented in the bycatch sample. Results from the two scenarios were "model averaged" by sampling from both Markov Chain Monte Carlo (MCMC) chains with uniform probability. The median estimate for potential population growth (r(nat)) was 0.046 (90% credible interval [CRI] = 0.004-0.116). The median for actual growth (r) was -0.030 (90% CRI = -0.192 to +0.065). The probability of population decline due to added

  15. Spatial analysis of distribution of dengue cases in Espírito Santo, Brazil, in 2010: use of Bayesian model

    Directory of Open Access Journals (Sweden)

    Taizi Honorato

    2014-01-01

    Full Text Available OBJECTIVE: To study the relationship between the risk of dengue and sociodemographic variables through the use of spatial regression models fully Bayesian in the municipalities of Espírito Santo in 2010. METHOD: This is an ecological study and exploration that used spatial analysis tools in preparing thematic maps with data obtained from SinanNet. An analysis by area, taking as unit the municipalities of the state, was performed. Thematic maps were constructed by the computer program R 2.15.00 and Deviance Information Criterion (DIC, calculated in WinBugs, Absolut and Normalized Mean Error (NMAE were the criteria used to compare the models. RESULTS: We were able to geocode 21,933 dengue cases (rate of 623.99 cases per 100 thousand habitants with a higher incidence in the municipalities of Vitória, Serra and Colatina; model with spatial effect with the covariates trash and income showed the best performance at DIC and Nmae criteria. CONCLUSION: It was possible to identify the relationship of dengue with factors outside the health sector and to identify areas with higher risk of disease.

  16. Linking bovine tuberculosis on cattle farms to white-tailed deer and environmental variables using Bayesian hierarchical analysis

    Science.gov (United States)

    Walter, William D.; Smith, Rick; Vanderklok, Mike; VerCauterren, Kurt C.

    2014-01-01

    Bovine tuberculosis is a bacterial disease caused by Mycobacterium bovis in livestock and wildlife with hosts that include Eurasian badgers (Meles meles), brushtail possum (Trichosurus vulpecula), and white-tailed deer (Odocoileus virginianus). Risk-assessment efforts in Michigan have been initiated on farms to minimize interactions of cattle with wildlife hosts but research onM. bovis on cattle farms has not investigated the spatial context of disease epidemiology. To incorporate spatially explicit data, initial likelihood of infection probabilities for cattle farms tested for M. bovis, prevalence of M. bovis in white-tailed deer, deer density, and environmental variables for each farm were modeled in a Bayesian hierarchical framework. We used geo-referenced locations of 762 cattle farms that have been tested for M. bovis, white-tailed deer prevalence, and several environmental variables that may lead to long-term survival and viability of M. bovis on farms and surrounding habitats (i.e., soil type, habitat type). Bayesian hierarchical analyses identified deer prevalence and proportion of sandy soil within our sampling grid as the most supported model. Analysis of cattle farms tested for M. bovisidentified that for every 1% increase in sandy soil resulted in an increase in odds of infection by 4%. Our analysis revealed that the influence of prevalence of M. bovis in white-tailed deer was still a concern even after considerable efforts to prevent cattle interactions with white-tailed deer through on-farm mitigation and reduction in the deer population. Cattle farms test positive for M. bovis annually in our study area suggesting that the potential for an environmental source either on farms or in the surrounding landscape may contributing to new or re-infections with M. bovis. Our research provides an initial assessment of potential environmental factors that could be incorporated into additional modeling efforts as more knowledge of deer herd

  17. Bayesian theory and applications

    CERN Document Server

    Dellaportas, Petros; Polson, Nicholas G; Stephens, David A

    2013-01-01

    The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...

  18. Prior approval: the growth of Bayesian methods in psychology.

    Science.gov (United States)

    Andrews, Mark; Baguley, Thom

    2013-02-01

    Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.

  19. A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation

    International Nuclear Information System (INIS)

    Trucco, P.; Cagno, E.; Ruggeri, F.; Grande, O.

    2008-01-01

    The paper presents an innovative approach to integrate Human and Organisational Factors (HOF) into risk analysis. The approach has been developed and applied to a case study in the maritime industry, but it can also be utilised in other sectors. A Bayesian Belief Network (BBN) has been developed to model the Maritime Transport System (MTS), by taking into account its different actors (i.e., ship-owner, shipyard, port and regulator) and their mutual influences. The latter have been modelled by means of a set of dependent variables whose combinations express the relevant functions performed by each actor. The BBN model of the MTS has been used in a case study for the quantification of HOF in the risk analysis carried out at the preliminary design stage of High Speed Craft (HSC). The study has focused on a collision in open sea hazard carried out by means of an original method of integration of a Fault Tree Analysis (FTA) of technical elements with a BBN model of the influences of organisational functions and regulations, as suggested by the International Maritime Organisation's (IMO) Guidelines for Formal Safety Assessment (FSA). The approach has allowed the identification of probabilistic correlations between the basic events of a collision accident and the BBN model of the operational and organisational conditions. The linkage can be exploited in different ways, especially to support identification and evaluation of risk control options also at the organisational level. Conditional probabilities for the BBN have been estimated by means of experts' judgments, collected from an international panel of different European countries. Finally, a sensitivity analysis has been carried out over the model to identify configurations of the MTS leading to a significant reduction of accident probability during the operation of the HSC

  20. Genetic origin and dispersal of the invasive soybean aphid inferred from population genetic analysis and approximate Bayesian computation.

    Science.gov (United States)

    Fang, Fang; Chen, Jing; Jiang, Li-Yun; Qu, Yan-Hua; Qiao, Ge-Xia

    2018-01-09

    Biological invasion is considered one of the most important global environmental problems. Knowledge of the source and dispersal routes of invasion could facilitate the eradication and control of invasive species. Soybean aphid, Aphis glycines Matsumura, is one of the most destructive soybean pests. For effective management of this pest, we conducted genetic analyses and approximate Bayesian computation (ABC) analysis to determine the origins and dispersal of the aphid species, as well as the source of its invasion in the USA, using eight microsatellite loci and the mitochondrial cytochrome c oxidase subunit I (COI) gene. We were able to identify a significant isolation by distance (IBD) pattern and three genetic lineages in the microsatellite data but not in the mtDNA dataset. The genetic structure showed that the USA population has the closest relationship with those from Korea and Japan, indicating that the two latter populations might be the sources of the invasion to the USA. Both population genetic analyses and ABC showed that the northeastern populations in China were the possible sources of the further spread of A. glycines to Indonesia. The dispersal history of this aphid can provide useful information for pest management strategies and can further help predict areas at risk of invasion. This article is protected by copyright. All rights reserved.

  1. Bayesian network meta-analysis suggests a similar effectiveness between a monovalent and a pentavalent rotavirus vaccine

    Science.gov (United States)

    Takeuchi, Masato

    2014-01-01

    To assess the comparative effectiveness of a monovalent and a pentavalent rotavirus vaccine (RV1 and RV5), a Bayesian network meta-analysis was conducted. Data of randomized trials from the Cochrane Review in 2012 were extracted and synthesized. For the prevention of severe rotavirus disease up to 2 years, no statistical difference was found in the effectiveness between the 2 types of vaccine (odds ratio: 2.23, 95% credible interval: 0.71–5.20). Similarly, the comparative effectiveness of RV1 and RV5 appeared equivalent for other rotavirus-associated outcome measures, such as prevention of severe disease up to 1 year and all severity of rotavirus infections for up to both 1- and 2-year follow-ups. These results indicates that, overall, RV1 and RV5 offer similar benefits to prevent rotavirus diseases; nonetheless, credible intervals are generally wide, highlighting the necessity of further meta-analyses including updated information or, ideally, controlled trials comparing both vaccines directly. PMID:24608099

  2. Comparative efficacy and acceptability of antiepileptic drugs for classical trigeminal neuralgia: a Bayesian network meta-analysis protocol.

    Science.gov (United States)

    Qin, Zongshi; Xie, Shang; Mao, Zhi; Liu, Yan; Wu, Jiani; Furukawa, Toshi A; Kwong, Joey S W; Tian, Jinhui; Liu, Zhishun

    2018-01-21

    Trigeminal neuralgia (TN) affects 4 to 28.9/100 000 people worldwide, and antiepileptic drugs such as carbamazepine and oxcarbazepine are the firstline treatment options. However, the efficacy and safety of other antiepileptic drugs remain unclear due to insufficient direct comparisons. To compare the efficacy and acceptability of all currently available antiepileptic agents for the treatment of patients with classical TN. We will search the PubMed, EMBASE, Cochrane Library and Web of Science databases for unpublished or undergoing research listed in registry platforms. We will include all randomised controlled trials comparing two different antiepileptic drugs or one antiepileptic drug with placebo in patients with classical TN. The primary outcomes will be the proportion of responders and the number of subjects who dropout during the treatment. The secondary outcomes will include the two primary outcomes but in the follow-up period, changes in the self-reporting assessment scale for neuralgia and quality of life assessment. In terms of network meta-analysis, we will fit our model to a Bayesian framework using the JAGS and pcnetmeta packages of the R project. This protocol will not disseminate any private patient data. The results of this review will be disseminated through peer reviewed publication. CRD42016048640. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  3. Population size and stopover duration estimation using mark-resight data and Bayesian analysis of a superpopulation model.

    Science.gov (United States)

    Lyons, James E; Kendall, William L; Royle, J Andrew; Converse, Sarah J; Andres, Brad A; Buchanan, Joseph B

    2016-03-01

    We present a novel formulation of a mark-recapture-resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state-space formulation of the Jolly-Seber mark-recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state-space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers. © 2015, The International Biometric Society.

  4. Population size and stopover duration estimation using mark–resight data and Bayesian analysis of a superpopulation model

    Science.gov (United States)

    Lyons, James E.; Kendall, William L.; Royle, J. Andrew; Converse, Sarah J.; Andres, Brad A.; Buchanan, Joseph B.

    2016-01-01

    We present a novel formulation of a mark–recapture–resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state–space formulation of the Jolly–Seber mark–recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state–space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers.

  5. Bayesian Analysis of a Lipid-Based Physiologically Based Toxicokinetic Model for a Mixture of PCBs in Rats

    Directory of Open Access Journals (Sweden)

    Alan F. Sasso

    2012-01-01

    Full Text Available A lipid-based physiologically based toxicokinetic (PBTK model has been developed for a mixture of six polychlorinated biphenyls (PCBs in rats. The aim of this study was to apply population Bayesian analysis to a lipid PBTK model, while incorporating an internal exposure-response model linking enzyme induction and metabolic rate. Lipid-based physiologically based toxicokinetic models are a subset of PBTK models that can simulate concentrations of highly lipophilic compounds in tissue lipids, without the need for partition coefficients. A hierarchical treatment of population metabolic parameters and a CYP450 induction model were incorporated into the lipid-based PBTK framework, and Markov-Chain Monte Carlo was applied to in vivo data. A mass balance of CYP1A and CYP2B in the liver was necessary to model PCB metabolism at high doses. The linked PBTK/induction model remained on a lipid basis and was capable of modeling PCB concentrations in multiple tissues for all dose levels and dose profiles.

  6. How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection.

    Science.gov (United States)

    Soch, Joram; Haynes, John-Dylan; Allefeld, Carsten

    2016-11-01

    Voxel-wise general linear models (GLMs) are a standard approach for analyzing functional magnetic resonance imaging (fMRI) data. An advantage of GLMs is that they are flexible and can be adapted to the requirements of many different data sets. However, the specification of first-level GLMs leaves the researcher with many degrees of freedom which is problematic given recent efforts to ensure robust and reproducible fMRI data analysis. Formal model comparisons that allow a systematic assessment of GLMs are only rarely performed. On the one hand, too simple models may underfit data and leave real effects undiscovered. On the other hand, too complex models might overfit data and also reduce statistical power. Here we present a systematic approach termed cross-validated Bayesian model selection (cvBMS) that allows to decide which GLM best describes a given fMRI data set. Importantly, our approach allows for non-nested model comparison, i.e. comparing more than two models that do not just differ by adding one or more regressors. It also allows for spatially heterogeneous modelling, i.e. using different models for different parts of the brain. We validate our method using simulated data and demonstrate potential applications to empirical data. The increased use of model comparison and model selection should increase the reliability of GLM results and reproducibility of fMRI studies. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Evaluation of non-animal methods for assessing skin sensitisation hazard: A Bayesian Value-of-Information analysis.

    Science.gov (United States)

    Leontaridou, Maria; Gabbert, Silke; Van Ierland, Ekko C; Worth, Andrew P; Landsiedel, Robert

    2016-07-01

    This paper offers a Bayesian Value-of-Information (VOI) analysis for guiding the development of non-animal testing strategies, balancing information gains from testing with the expected social gains and costs from the adoption of regulatory decisions. Testing is assumed to have value, if, and only if, the information revealed from testing triggers a welfare-improving decision on the use (or non-use) of a substance. As an illustration, our VOI model is applied to a set of five individual non-animal prediction methods used for skin sensitisation hazard assessment, seven battery combinations of these methods, and 236 sequential 2-test and 3-test strategies. Their expected values are quantified and compared to the expected value of the local lymph node assay (LLNA) as the animal method. We find that battery and sequential combinations of non-animal prediction methods reveal a significantly higher expected value than the LLNA. This holds for the entire range of prior beliefs. Furthermore, our results illustrate that the testing strategy with the highest expected value does not necessarily have to follow the order of key events in the sensitisation adverse outcome pathway (AOP). 2016 FRAME.

  8. Bayesian artificial intelligence

    CERN Document Server

    Korb, Kevin B

    2003-01-01

    As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.

  9. Bayesian artificial intelligence

    CERN Document Server

    Korb, Kevin B

    2010-01-01

    Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente

  10. Global trends and factors associated with the illegal killing of elephants: A hierarchical bayesian analysis of carcass encounter data.

    Directory of Open Access Journals (Sweden)

    Robert W Burn

    Full Text Available Elephant poaching and the ivory trade remain high on the agenda at meetings of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES. Well-informed debates require robust estimates of trends, the spatial distribution of poaching, and drivers of poaching. We present an analysis of trends and drivers of an indicator of elephant poaching of all elephant species. The site-based monitoring system known as Monitoring the Illegal Killing of Elephants (MIKE, set up by the 10(th Conference of the Parties of CITES in 1997, produces carcass encounter data reported mainly by anti-poaching patrols. Data analyzed were site by year totals of 6,337 carcasses from 66 sites in Africa and Asia from 2002-2009. Analysis of these observational data is a serious challenge to traditional statistical methods because of the opportunistic and non-random nature of patrols, and the heterogeneity across sites. Adopting a bayesian hierarchical modeling approach, we used the proportion of carcasses that were illegally killed (PIKE as a poaching index, to estimate the trend and the effects of site- and country-level factors associated with poaching. Important drivers of illegal killing that emerged at country level were poor governance and low levels of human development, and at site level, forest cover and area of the site in regions where human population density is low. After a drop from 2002, PIKE remained fairly constant from 2003 until 2006, after which it increased until 2008. The results for 2009 indicate a decline. Sites with PIKE ranging from the lowest to the highest were identified. The results of the analysis provide a sound information base for scientific evidence-based decision making in the CITES process.

  11. Paleotempestological chronology developed from gas ion source AMS analysis of carbonates determined through real-time Bayesian statistical approach

    Science.gov (United States)

    Wallace, D. J.; Rosenheim, B. E.; Roberts, M. L.; Burton, J. R.; Donnelly, J. P.; Woodruff, J. D.

    2014-12-01

    Is a small quantity of high-precision ages more robust than a higher quantity of lower-precision ages for sediment core chronologies? AMS Radiocarbon ages have been available to researchers for several decades now, and precision of the technique has continued to improve. Analysis and time cost is high, though, and projects are often limited in terms of the number of dates that can be used to develop a chronology. The Gas Ion Source at the National Ocean Sciences Accelerator Mass Spectrometry Facility (NOSAMS), while providing lower-precision (uncertainty of order 100 14C y for a sample), is significantly less expensive and far less time consuming than conventional age dating and offers the unique opportunity for large amounts of ages. Here we couple two approaches, one analytical and one statistical, to investigate the utility of an age model comprised of these lower-precision ages for paleotempestology. We use a gas ion source interfaced to a gas-bench type device to generate radiocarbon dates approximately every 5 minutes while determining the order of sample analysis using the published Bayesian accumulation histories for deposits (Bacon). During two day-long sessions, several dates were obtained from carbonate shells in living position in a sediment core comprised of sapropel gel from Mangrove Lake, Bermuda. Samples were prepared where large shells were available, and the order of analysis was determined by the depth with the highest uncertainty according to Bacon. We present the results of these analyses as well as a prognosis for a future where such age models can be constructed from many dates that are quickly obtained relative to conventional radiocarbon dates. This technique currently is limited to carbonates, but development of a system for organic material dating is underway. We will demonstrate the extent to which sacrificing some analytical precision in favor of more dates improves age models.

  12. Effects of Hazelnut Consumption on Blood Lipids and Body Weight: A Systematic Review and Bayesian Meta-Analysis

    Directory of Open Access Journals (Sweden)

    Simone Perna

    2016-11-01

    Full Text Available Hazelnuts are rich in monounsaturated fatty acids and antioxidant bioactive substances: their consumption has been associated with a decreased risk of cardiovascular disease events. A systematic review and a meta-analysis was performed to combine the results from several trials and to estimate the pooled (overall effect of hazelnuts on blood lipids and body weight outcomes. Specifically, a Bayesian random effect meta-analysis of mean differences of Δ-changes from baseline across treatment (MDΔ (i.e., hazelnut-enriched diet vs. control diet has been conducted. Nine studies representing 425 participants were included in the analysis. The intervention diet lasted 28–84 days with a dosage of hazelnuts ranging from 29 to 69 g/day. Out of nine studies, three randomized studies have been meta-analyzed showing a significant reduction in low-density lipoprotein (LDL cholesterol (pooled MDΔ = −0.150 mmol/L; 95% highest posterior density interval (95%HPD = −0.308; −0.003 in favor of a hazelnut-enriched diet. Total cholesterol showed a marked trend toward a decrease (pooled MDΔ = −0.127 mmol/L; 95%HPD = −0.284; 0.014 and high-density lipoprotein (HDL cholesterol remained substantially stable (pooled MDΔ = 0.002 mmol/L; 95%HPD = −0.140; 0.147. No effects on triglycerides (pooled MDΔ = 0.045 mmol/L; 95%HPD = −0.195; 0.269 and body mass index (BMI (pooled MDΔ = 0.062 kg/m2; 95%HPD = −0.293; 0.469 were found. Hazelnut-enriched diet is associated with a decrease of LDL and total cholesterol, while HDL cholesterol, triglycerides and BMI remain substantially unchanged.

  13. Spiritual and ceremonial plants in North America: an assessment of Moerman's ethnobotanical database comparing Residual, Binomial, Bayesian and Imprecise Dirichlet Model (IDM) analysis.

    Science.gov (United States)

    Turi, Christina E; Murch, Susan J

    2013-07-09

    Ethnobotanical research and the study of plants used for rituals, ceremonies and to connect with the spirit world have led to the discovery of many novel psychoactive compounds such as nicotine, caffeine, and cocaine. In North America, spiritual and ceremonial uses of plants are well documented and can be accessed online via the University of Michigan's Native American Ethnobotany Database. The objective of the study was to compare Residual, Bayesian, Binomial and Imprecise Dirichlet Model (IDM) analyses of ritual, ceremonial and spiritual plants in Moerman's ethnobotanical database and to identify genera that may be good candidates for the discovery of novel psychoactive compounds. The database was queried with the following format "Family Name AND Ceremonial OR Spiritual" for 263 North American botanical families. Spiritual and ceremonial flora consisted of 86 families with 517 species belonging to 292 genera. Spiritual taxa were then grouped further into ceremonial medicines and items categories. Residual, Bayesian, Binomial and IDM analysis were performed to identify over and under-utilized families. The 4 statistical approaches were in good agreement when identifying under-utilized families but large families (>393 species) were underemphasized by Binomial, Bayesian and IDM approaches for over-utilization. Residual, Binomial, and IDM analysis identified similar families as over-utilized in the medium (92-392 species) and small (Binomial analysis when separated into small, medium and large families. The Bayesian, Binomial and IDM approaches identified different genera as potentially important. Species belonging to the genus Artemisia and Ligusticum were most consistently identified and may be valuable in future studies of the ethnopharmacology. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  14. Applied Bayesian modelling

    CERN Document Server

    Congdon, Peter

    2014-01-01

    This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBU

  15. Alveolar ridge preservation after tooth extraction: a Bayesian Network meta-analysis of grafting materials efficacy on prevention of bone height and width reduction.

    Science.gov (United States)

    Iocca, Oreste; Farcomeni, Alessio; Pardiñas Lopez, Simon; Talib, Huzefa S

    2017-01-01

    To conduct a traditional meta-analysis and a Bayesian Network meta-analysis to synthesize the information coming from randomized controlled trials on different socket grafting materials and combine the resulting indirect evidence in order to make inferences on treatments that have not been compared directly. RCTs were identified for inclusion in the systematic review and subsequent statistical analysis. Bone height and width remodelling were selected as the chosen summary measures for comparison. First, a series of pairwise meta-analyses were performed and overall mean difference (MD) in mm with 95% CI was calculated between grafted versus non-grafted sockets. Then, a Bayesian Network meta-analysis was performed to draw indirect conclusions on which grafting materials can be considered most likely the best compared to the others. From the six included studies, seven comparisons were obtained. Traditional meta-analysis showed statistically significant results in favour of grafting the socket compared to no-graft both for height (MD 1.02, 95% CI 0.44-1.59, p value meta-analysis allowed to obtain a rank of intervention efficacy. On the basis of the results of the present analysis, socket grafting seems to be more favourable than unassisted socket healing. Moreover, Bayesian Network meta-analysis indicates that freeze-dried bone graft plus membrane is the most likely effective in the reduction of bone height remodelling. Autologous bone marrow resulted the most likely effective when width remodelling was considered. Studies with larger samples and less risk of bias should be conducted in the future in order to further strengthen the results of this analysis. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  16. Bayesian sensitivity analysis of a 1D vascular model with Gaussian process emulators.

    Science.gov (United States)

    Melis, Alessandro; Clayton, Richard H; Marzo, Alberto

    2017-12-01

    One-dimensional models of the cardiovascular system can capture the physics of pulse waves but involve many parameters. Since these may vary among individuals, patient-specific models are difficult to construct. Sensitivity analysis can be used to rank model parameters by their effect on outputs and to quantify how uncertainty in parameters influences output uncertainty. This type of analysis is often conducted with a Monte Carlo method, where large numbers of model runs are used to assess input-output relations. The aim of this study was to demonstrate the computational efficiency of variance-based sensitivity analysis of 1D vascular models using Gaussian process emulators, compared to a standard Monte Carlo approach. The methodology was tested on four vascular networks of increasing complexity to analyse its scalability. The computational time needed to perform the sensitivity analysis with an emulator was reduced by the 99.96% compared to a Monte Carlo approach. Despite the reduced computational time, sensitivity indices obtained using the two approaches were comparable. The scalability study showed that the number of mechanistic simulations needed to train a Gaussian process for sensitivity analysis was of the order O(d), rather than O(d×103) needed for Monte Carlo analysis (where d is the number of parameters in the model). The efficiency of this approach, combined with capacity to estimate the impact of uncertain parameters on model outputs, will enable development of patient-specific models of the vascular system, and has the potential to produce results with clinical relevance. © 2017 The Authors International Journal for Numerical Methods in Biomedical Engineering Published by John Wiley & Sons Ltd.

  17. Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models

    DEFF Research Database (Denmark)

    Klein, Hans-Ulrich; Schäfer, Martin; Porse, Bo T

    2014-01-01

    Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel...

  18. Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems

    Science.gov (United States)

    He, Yuning; Davies, Misty Dawn

    2014-01-01

    The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.

  19. Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection

    OpenAIRE

    Dhavala, Soma S.; Datta, Sujay; Mallick, Bani K.; Carroll, Raymond J.; Khare, Sangeeta; Lawhon, Sara D.; Adams, L. Garry

    2010-01-01

    Massively Parallel Signature Sequencing (MPSS) is a high-throughput counting-based technology available for gene expression profiling. It produces output that is similar to Serial Analysis of Gene Expression (SAGE) and is ideal for building complex relational databases for gene expression. Our goal is to compare the in vivo global gene expression profiles of tissues infected with different strains of Salmonella obtained using the MPSS technology. In this article, we develop an exact ANOVA typ...

  20. A semi-parametric Bayesian model for unsupervised differential co-expression analysis

    Directory of Open Access Journals (Sweden)

    Medvedovic Mario

    2010-05-01

    Full Text Available Abstract Background Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples. Results We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is concordant within these samples. To the best of our knowledge, this is the first method to date for unsupervised differential co-expression analysis in this generality. When applied to the problem of identifying molecular subtypes of breast cancer, our method identified reproducible patterns of differential co-expression across several independent expression datasets. Sample groupings induced by these patterns were highly informative of the disease outcome. Expression patterns of differentially co-expressed genes provided new insights into the complex nature of the ERα regulatory network. Conclusions We demonstrated that the use of the co-clustering structure as the similarity measure in the unsupervised analysis of sample gene expression profiles provides valuable information about expression regulatory networks.

  1. Meta-analysis of candidate gene effects using bayesian parametric and non-parametric approaches.

    Science.gov (United States)

    Wu, Xiao-Lin; Gianola, Daniel; Rosa, Guilherme J M; Weigel, Kent A

    2014-01-01

    Candidate gene (CG) approaches provide a strategy for identification and characterization of major genes underlying complex phenotypes such as production traits and susceptibility to diseases, but the conclusions tend to be inconsistent across individual studies. Meta-analysis approaches can deal with these situations, e.g., by pooling effect-size estimates or combining P values from multiple studies. In this paper, we evaluated the performance of two types of statistical models, parametric and non-parametric, for meta-analysis of CG effects using simulated data. Both models estimated a "central" effect size while taking into account heterogeneity over individual studies. The empirical distribution of study-specific CG effects was multi-modal. The parametric model assumed a normal distribution for the study-specific CG effects whereas the non-parametric model relaxed this assumption by posing a more general distribution with a Dirichlet process prior (DPP). Results indicated that the meta-analysis approaches could reduce false positive or false negative rates by pooling strengths from multiple studies, as compared to individual studies. In addition, the non-parametric, DPP model captured the variation of the "data" better than its parametric counterpart.

  2. Carboy Security Risk Analysis Model of I and C System Using Bayesian Network

    International Nuclear Information System (INIS)

    Shin, Jinsoo; Heo, Gyunyoung; Son, Hanseong; Park, Jaekwan

    2013-01-01

    The Korea Institute of Nuclear Safety (KINS) as a regulatory agency declares the R. G 8.22 for applying cyber security in Korea in 2011. In nuclear power industrial, ShinUljin 1, 2 unit and Shingori 3, 4 unit are demonstrating the cyber security for the first time. And in terms of research, the National Security Research Institute and the Korea Atomic Energy Research Institute are developing the nuclear power plant cyber security system in Korean. Currently, these cyber securities like regulation, demonstration and research are focused on nuclear power plant. However, cyber security is also important for the nuclear research reactor like a HANARO which is in Daejeon, primarily due to its characteristic as research reactor since since people access more than power plant. Analysis of the key elements of cyber security is possible to study through the activity-quality and architecture analysis model of cyber security. It is possible to analyze the extent reflected final risk by evaluating input score for each checklist. In this way, you can see an important checklist. Further, if the cyber-attack occurs, it is possible to provide an evidentiary material that is able to determine the key check element corresponding to each situation via a reverse calculation of BN. Finally, Utilization is possible to create a simulated penetratio test scenario according to each situation. Analysis of the key elements of cyber security is possible to study through the activity-quality and architecture analysis model of cyber security. It is possible to analyze the extent reflected in the final risk by evaluating input score for each checklist, in this way, you can see an important checklist. Furthermore, if the cyber-attack occurs, it is possible to provide an evidentiary material that enables to determine the key check element corresponding to each situation via a reverse calculation of BN. Finally, Utilization is possible to create a simulated penetration test scenario according to

  3. Strong approximations and sequential change-point analysis for diffusion processes

    DEFF Research Database (Denmark)

    Mihalache, Stefan-Radu

    2012-01-01

    In this paper ergodic diffusion processes depending on a parameter in the drift are considered under the assumption that the processes can be observed continuously. Strong approximations by Wiener processes for a stochastic integral and for the estimator process constructed by the one...

  4. Non-invasive clinical parameters for the prediction of urodynamic bladder outlet obstruction: analysis using causal Bayesian networks.

    Directory of Open Access Journals (Sweden)

    Myong Kim

    Full Text Available To identify non-invasive clinical parameters to predict urodynamic bladder outlet obstruction (BOO in patients with benign prostatic hyperplasia (BPH using causal Bayesian networks (CBN.From October 2004 to August 2013, 1,381 eligible BPH patients with complete data were selected for analysis. The following clinical variables were considered: age, total prostate volume (TPV, transition zone volume (TZV, prostate specific antigen (PSA, maximum flow rate (Qmax, and post-void residual volume (PVR on uroflowmetry, and International Prostate Symptom Score (IPSS. Among these variables, the independent predictors of BOO were selected using the CBN model. The predictive performance of the CBN model using the selected variables was verified through a logistic regression (LR model with the same dataset.Mean age, TPV, and IPSS were 6.2 (±7.3, SD years, 48.5 (±25.9 ml, and 17.9 (±7.9, respectively. The mean BOO index was 35.1 (±25.2 and 477 patients (34.5% had urodynamic BOO (BOO index ≥40. By using the CBN model, we identified TPV, Qmax, and PVR as independent predictors of BOO. With these three variables, the BOO prediction accuracy was 73.5%. The LR model showed a similar accuracy (77.0%. However, the area under the receiver operating characteristic curve of the CBN model was statistically smaller than that of the LR model (0.772 vs. 0.798, p = 0.020.Our study demonstrated that TPV, Qmax, and PVR are independent predictors of urodynamic BOO.

  5. Trends and inequalities in cardiovascular disease mortality across 7932 English electoral wards, 1982-2006: Bayesian spatial analysis.

    Science.gov (United States)

    Asaria, Perviz; Fortunato, Lea; Fecht, Daniela; Tzoulaki, Ioanna; Abellan, Juan Jose; Hambly, Peter; de Hoogh, Kees; Ezzati, Majid; Elliott, Paul

    2012-12-01

    Cardiovascular disease (CVD) mortality has more than halved in England since the 1980s, but there are few data on small-area trends. We estimated CVD mortality by ward in 5-year intervals between 1982 and 2006, and examined trends in relation to starting mortality, region and community deprivation. We analysed CVD death rates using a Bayesian spatial technique for all 7932 English electoral wards in consecutive 5-year intervals between 1982 and 2006, separately for men and women aged 30-64 years and ≥65 years. Age-standardized CVD mortality declined in the majority of wards, but increased in 186 wards for women aged ≥65 years. The decline was larger where starting mortality had been higher. When grouped by deprivation quintile, absolute inequality between most- and least-deprived wards narrowed over time in those aged 30-64 years, but increased in older adults; relative inequalities worsened in all four age-sex groups. Wards with high CVD mortality in 2002-06 fell into two groups: those in and around large metropolitan cities in northern England that started with high mortality in 1982-86 and could not 'catch up', despite impressive declines, and those that started with average or low mortality in the 1980s but 'fell behind' because of small mortality reductions. Improving population health and reducing health inequalities should be treated as related policy and measurement goals. Ongoing analysis of mortality by small area is essential to monitor local effects on health and health inequalities of the public health and healthcare systems.

  6. Trends and inequalities in cardiovascular disease mortality across 7932 English electoral wards, 1982–2006: Bayesian spatial analysis

    Science.gov (United States)

    Asaria, Perviz; Fortunato, Lea; Fecht, Daniela; Tzoulaki, Ioanna; Abellan, Juan Jose; Hambly, Peter; de Hoogh, Kees; Ezzati, Majid; Elliott, Paul

    2012-01-01

    Background Cardiovascular disease (CVD) mortality has more than halved in England since the 1980s, but there are few data on small-area trends. We estimated CVD mortality by ward in 5-year intervals between 1982 and 2006, and examined trends in relation to starting mortality, region and community deprivation. Methods We analysed CVD death rates using a Bayesian spatial technique for all 7932 English electoral wards in consecutive 5-year intervals between 1982 and 2006, separately for men and women aged 30–64 years and ≥65 years. Results Age-standardized CVD mortality declined in the majority of wards, but increased in 186 wards for women aged ≥65 years. The decline was larger where starting mortality had been higher. When grouped by deprivation quintile, absolute inequality between most- and least-deprived wards narrowed over time in those aged 30–64 years, but increased in older adults; relative inequalities worsened in all four age–sex groups. Wards with high CVD mortality in 2002–06 fell into two groups: those in and around large metropolitan cities in northern England that started with high mortality in 1982–86 and could not ‘catch up’, despite impressive declines, and those that started with average or low mortality in the 1980s but ‘fell behind’ because of small mortality reductions. Conclusions Improving population health and reducing health inequalities should be treated as related policy and measurement goals. Ongoing analysis of mortality by small area is essential to monitor local effects on health and health inequalities of the public health and healthcare systems. PMID:23129720

  7. By-catch of grey seals (Halichoerus grypus) in Baltic fisheries--a Bayesian analysis of interview survey.

    Science.gov (United States)

    Vanhatalo, Jarno; Vetemaa, Markus; Herrero, Annika; Aho, Teija; Tiilikainen, Raisa

    2014-01-01

    Baltic seals are recovering after a population decline. The increasing seal stocks cause notable damage to fisheries in the Baltic Sea, with an unknown number of seals drowning in fishing gear every year. Thus, sustainable seal management requires updated knowledge of the by-catch of seals--the number of specimens that die in fishing gear. We analyse the by-catch of grey seals (Halichoerus grypus) in Finland, Sweden, and Estonia in 2012. We collect data with interviews (35 in Finland, 54 in Sweden, and 72 in Estonia) and analyse them with a hierarchical Bayesian model. The model accounts for variability in seal abundance, seal mortality and fishing effort in different sub-areas of the Baltic Sea and allows us to predict the by-catch in areas where interview data was not available. We provide a detailed description of the survey design and interview methods, and discuss different factors affecting fishermen's motivation to report by-catch and how this may affect the results. Our analysis shows that the total yearly by-catch by trap and gill nets in Finland, Sweden and Estonia is, with 90% probability, more than 1240 but less than 2860; and the posterior median and mean of the total by-catch are 1550 and 1880 seals, respectively. Trap nets make about 88% of the total by-catch. However, results also indicate that in one of the sub-areas of this study, fishermen may have underreported their by-catch. Taking the possible underreporting into account the posterior mean of the total by-catch is between 2180 and 2380. The by-catch in our study area is likely to represent at least 90% of the total yearly grey seal by-catch in the Baltic Sea.

  8. By-Catch of Grey Seals (Halichoerus grypus) in Baltic Fisheries—A Bayesian Analysis of Interview Survey

    Science.gov (United States)

    Vanhatalo, Jarno; Vetemaa, Markus; Herrero, Annika; Aho, Teija; Tiilikainen, Raisa

    2014-01-01

    Baltic seals are recovering after a population decline. The increasing seal stocks cause notable damage to fisheries in the Baltic Sea, with an unknown number of seals drowning in fishing gear every year. Thus, sustainable seal management requires updated knowledge of the by-catch of seals—the number of specimens that die in fishing gear. We analyse the by-catch of grey seals (Halichoerus grypus) in Finland, Sweden, and Estonia in 2012. We collect data with interviews (35 in Finland, 54 in Sweden, and 72 in Estonia) and analyse them with a hierarchical Bayesian model. The model accounts for variability in seal abundance, seal mortality and fishing effort in different sub-areas of the Baltic Sea and allows us to predict the by-catch in areas where interview data was not available. We provide a detailed description of the survey design and interview methods, and discuss different factors affecting fishermen's motivation to report by-catch and how this may affect the results. Our analysis shows that the total yearly by-catch by trap and gill nets in Finland, Sweden and Estonia is, with 90% probability, more than 1240 but less than 2860; and the posterior median and mean of the total by-catch are 1550 and 1880 seals, respectively. Trap nets make about 88% of the total by-catch. However, results also indicate that in one of the sub-areas of this study, fishermen may have underreported their by-catch. Taking the possible underreporting into account the posterior mean of the total by-catch is between 2180 and 2380. The by-catch in our study area is likely to represent at least 90% of the total yearly grey seal by-catch in the Baltic Sea. PMID:25423168

  9. On the Risks of Belonging to Disadvantaged Groups: A Bayesian Analysis of Labour Market Outcomes

    OpenAIRE

    Borooah, Vani

    2010-01-01

    Although methods of analysis based on Bayes’ theorem have had rich applications in Law and in Medicine they have not been much used in Economics. We use Bayes’ theorem to construct two concepts of the “risk” associated with belonging to a particular group in terms of a favourable labour market outcome; this, in the Indian context, is taken as being in “regular employment”. The first concept, the Employment Risk Ratio, measures the odds of a person being in regular employment to being in non-...

  10. Monitoring county-level chlamydia incidence in Texas, 2004 – 2005: application of empirical Bayesian smoothing and Exploratory Spatial Data Analysis (ESDA methods

    Directory of Open Access Journals (Sweden)

    Owens Chantelle J

    2009-02-01

    Full Text Available Abstract Background Chlamydia continues to be the most prevalent disease in the United States. Effective spatial monitoring of chlamydia incidence is important for successful implementation of control and prevention programs. The objective of this study is to apply Bayesian smoothing and exploratory spatial data analysis (ESDA methods to monitor Texas county-level chlamydia incidence rates by examining spatiotemporal patterns. We used county-level data on chlamydia incidence (for all ages, gender and races from the National Electronic Telecommunications System for Surveillance (NETSS for 2004 and 2005. Results Bayesian-smoothed chlamydia incidence rates were spatially dependent both in levels and in relative changes. Erath county had significantly (p 300 cases per 100,000 residents than its contiguous neighbors (195 or less in both years. Gaines county experienced the highest relative increase in smoothed rates (173% – 139 to 379. The relative change in smoothed chlamydia rates in Newton county was significantly (p Conclusion Bayesian smoothing and ESDA methods can assist programs in using chlamydia surveillance data to identify outliers, as well as relevant changes in chlamydia incidence in specific geographic units. Secondly, it may also indirectly help in assessing existing differences and changes in chlamydia surveillance systems over time.

  11. Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data

    Directory of Open Access Journals (Sweden)

    Raftery Adrian E

    2009-02-01

    Full Text Available Abstract Background Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes. Results We applied the iterative BMA algorithm to two cancer datasets: breast cancer and diffuse large B-cell lymphoma (DLBCL data. On the breast cancer data, the algorithm selected a total of 15 predictor genes across 84 contending models from the training data. The maximum likelihood estimates of the selected genes and the posterior probabilities of the selected models from the training data were used to divide patients in the test (or validation dataset into high- and low-risk categories. Using the genes and models determined from the training data, we assigned patients from the test data into highly distinct risk groups (as indicated by a p-value of 7.26e-05 from the log-rank test. Moreover, we achieved comparable results using only the 5 top selected genes with 100% posterior probabilities. On the DLBCL data, our iterative BMA procedure selected a total of 25 genes across 3 contending models from the training data. Once again, we assigned the patients in the validation set to significantly distinct risk groups (p

  12. Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation

    Directory of Open Access Journals (Sweden)

    Joseph P. Yurko

    2015-01-01

    Full Text Available System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC sampling feasible. This work uses Gaussian Process (GP based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.

  13. A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data.

    Science.gov (United States)

    Liang, Faming; Kim, Jinsu; Song, Qifan

    2016-01-01

    Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-combine method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. This is illustrated in the paper by the tempering BMH algorithm, which can be viewed as a combination of parallel tempering and the BMH algorithm. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively.

  14. A Bootstrap Metropolis–Hastings Algorithm for Bayesian Analysis of Big Data

    Science.gov (United States)

    Kim, Jinsu; Song, Qifan

    2016-01-01

    Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-combine method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. This is illustrated in the paper by the tempering BMH algorithm, which can be viewed as a combination of parallel tempering and the BMH algorithm. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively. PMID:29033469

  15. On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis.

    Science.gov (United States)

    Yamazaki, Keisuke

    2012-07-01

    Parametric models for sequential data, such as hidden Markov models, stochastic context-free grammars, and linear dynamical systems, are widely used in time-series analysis and structural data analysis. Computation of the likelihood function is one of primary considerations in many learning methods. Iterative calculation of the likelihood such as the model selection is still time-consuming though there are effective algorithms based on dynamic programming. The present paper studies parameter learning in a simplified feature space to reduce the computational cost. Simplifying data is a common technique seen in feature selection and dimension reduction though an oversimplified space causes adverse learning results. Therefore, we mathematically investigate a condition of the feature map to have an asymptotically equivalent convergence point of estimated parameters, referred to as the vicarious map. As a demonstration to find vicarious maps, we consider the feature space, which limits the length of data, and derive a necessary length for parameter learning in hidden Markov models. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. A Bayesian Nonparametric Approach for the Analysis of Multiple Categorical Item Responses.

    Science.gov (United States)

    Waters, Andrew; Fronczyk, Kassandra; Guindani, Michele; Baraniuk, Richard G; Vannucci, Marina

    2015-11-01

    We develop a modeling framework for joint factor and cluster analysis of datasets where multiple categorical response items are collected on a heterogeneous population of individuals. We introduce a latent factor multinomial probit model and employ prior constructions that allow inference on the number of factors as well as clustering of the subjects into homogenous groups according to their relevant factors. Clustering, in particular, allows us to borrow strength across subjects, therefore helping in the estimation of the model parameters, particularly when the number of observations is small. We employ Markov chain Monte Carlo techniques and obtain tractable posterior inference for our objectives, including sampling of missing data. We demonstrate the effectiveness of our method on simulated data. We also analyze two real-world educational datasets and show that our method outperforms state-of-the-art methods. In the analysis of the real-world data, we uncover hidden relationships between the questions and the underlying educational concepts, while simultaneously partitioning the students into groups of similar educational mastery.

  17. Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables

    DEFF Research Database (Denmark)

    Burgess, Stephen; Thompson, Simon G; Thompson, Grahame

    2010-01-01

    Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context...... an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide...... a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes....

  18. Bayesian Analysis of Item Response Curves. Research Report 84-1. Mathematical Sciences Technical Report No. 132.

    Science.gov (United States)

    Tsutakawa, Robert K.; Lin, Hsin Ying

    Item response curves for a set of binary responses are studied from a Bayesian viewpoint of estimating the item parameters. For the two-parameter logistic model with normally distributed ability, restricted bivariate beta priors are used to illustrate the computation of the posterior mode via the EM algorithm. The procedure is illustrated by data…

  19. The historical biogeography of Pteroglossus aracaris (Aves, Piciformes, Ramphastidae based on Bayesian analysis of mitochondrial DNA sequences

    Directory of Open Access Journals (Sweden)

    Sérgio L. Pereira

    2008-01-01

    Full Text Available Most Neotropical birds, including Pteroglossus aracaris, do not have an adequate fossil record to be used as time constraints in molecular dating. Hence, the evolutionary timeframe of the avian biota can only be inferred using alternative time constraints. We applied a Bayesian relaxed clock approach to propose an alternative interpretation for the historical biogeography of Pteroglossus based on mitochondrial DNA sequences, using different combinations of outgroups and time constraints obtained from outgroup fossils, vicariant barriers and molecular time estimates. The results indicated that outgroup choice has little effect on the Bayesian posterior distribution of divergence times within Pteroglossus , that geological and molecular time constraints seem equally suitable to estimate the Bayesian posterior distribution of divergence times for Pteroglossus , and that the fossil record alone overestimates divergence times within the fossil-lacking ingroup. The Bayesian estimates of divergence times suggest that the radiation of Pteroglossus occurred from the Late Miocene to the Pliocene (three times older than estimated by the “standard” mitochondrial rate of 2% sequence divergence per million years, likely triggered by Andean uplift, multiple episodes of marine transgressions in South America, and formation of present-day river basins. The time estimates are in agreement with other Neotropical taxa with similar geographic distributions.

  20. Bayesian flood forecasting methods: A review

    Science.gov (United States)

    Han, Shasha; Coulibaly, Paulin

    2017-08-01

    Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been

  1. Bayesian networks in levee reliability

    NARCIS (Netherlands)

    Roscoe, K.; Hanea, A.

    2015-01-01

    We applied a Bayesian network to a system of levees for which the results of traditional reliability analysis showed high failure probabilities, which conflicted with the intuition and experience of those managing the levees. We made use of forty proven strength observations - high water levels with

  2. Association between LRP5 polymorphism and bone mineral density: a Bayesian meta-analysis

    Directory of Open Access Journals (Sweden)

    Eisman John A

    2008-06-01

    Full Text Available Abstract Background The low-density lipoprotein receptor-related protein 5 gene (LRP5 was identified to be linked to the variation in BMD in high bone mass pedigrees. Subsequent population-based studies of the association between the LRP5 gene and BMD have yielded conflicting results. The present study was aimed at examining the association between LRP5 gene and BMD by using meta-analysis. Methods A systematic electronic search of literature was conducted to identify all published studies in English on the association between LRP5 gene and osteoporosis-related phenotypes, including bone mineral density and fracture. BMD data were summarized from individual studies by LRP5 genotype, and a synthesis of data was performed with random-effects meta-analyses. After excluding studies on animal and review papers, there were 19 studies for the synthesis. Among these studies, 10 studies used the rs3736228 (A1330V polymorphism and reported BMD values. Results The 10 eligible studies comprised 16,705 individuals, with the majority being women (n = 8444, aged between 18 – 81 years. The overall distribution of genotype frequencies was: AA, 68%, AV and VV, 32%. However, the genotype frequency varied significantly within as well as between ethnic populations. On random-effects meta-analysis, lumbar spine BMD among individuals with the AA genotype was on average 0.018 (95% confidence interval [CI]: 0.012 to 0.023 g/cm2 higher than those with either AV or VV genotype. Similarly, femoral neck BMD among carriers of the AA genotype was 0.011 (95%CI: 0.004 to 0.017 g/cm2 higher than those without the genotype. While there was no significant heterogeneity in the association between the A1330V polymorphism and lumbar spine BMD (p = 0.55, the association was heterogeneous for femoral neck BMD (p = 0.05. The probability that the difference is greater than one standard deviation was 0.34 for femoral neck BMD and 0.54 for lumbar spine BMD. Conclusion These results

  3. Association between LRP5 polymorphism and bone mineral density: a Bayesian meta-analysis.

    Science.gov (United States)

    Tran, Bich N H; Nguyen, Nguyen D; Eisman, John A; Nguyen, Tuan V

    2008-06-27

    The low-density lipoprotein receptor-related protein 5 gene (LRP5) was identified to be linked to the variation in BMD in high bone mass pedigrees. Subsequent population-based studies of the association between the LRP5 gene and BMD have yielded conflicting results. The present study was aimed at examining the association between LRP5 gene and BMD by using meta-analysis. A systematic electronic search of literature was conducted to identify all published studies in English on the association between LRP5 gene and osteoporosis-related phenotypes, including bone mineral density and fracture. BMD data were summarized from individual studies by LRP5 genotype, and a synthesis of data was performed with random-effects meta-analyses. After excluding studies on animal and review papers, there were 19 studies for the synthesis. Among these studies, 10 studies used the rs3736228 (A1330V) polymorphism and reported BMD values. The 10 eligible studies comprised 16,705 individuals, with the majority being women (n = 8444), aged between 18 - 81 years. The overall distribution of genotype frequencies was: AA, 68%, AV and VV, 32%. However, the genotype frequency varied significantly within as well as between ethnic populations. On random-effects meta-analysis, lumbar spine BMD among individuals with the AA genotype was on average 0.018 (95% confidence interval [CI]: 0.012 to 0.023) g/cm2 higher than those with either AV or VV genotype. Similarly, femoral neck BMD among carriers of the AA genotype was 0.011 (95%CI: 0.004 to 0.017) g/cm2 higher than those without the genotype. While there was no significant heterogeneity in the association between the A1330V polymorphism and lumbar spine BMD (p = 0.55), the association was heterogeneous for femoral neck BMD (p = 0.05). The probability that the difference is greater than one standard deviation was 0.34 for femoral neck BMD and 0.54 for lumbar spine BMD. These results suggest that there is a modest effect of the A1330V polymorphism on

  4. Associations between sexual habits, menstrual hygiene practices, demographics and the vaginal microbiome as revealed by Bayesian network analysis.

    Directory of Open Access Journals (Sweden)

    Noelle Noyes

    Full Text Available The vaginal microbiome plays an influential role in several disease states in reproductive age women, including bacterial vaginosis (BV. While demographic characteristics are associated with differences in vaginal microbiome community structure, little is known about the influence of sexual and hygiene habits. Furthermore, associations between the vaginal microbiome and risk symptoms of bacterial vaginosis have not been fully elucidated. Using Bayesian network (BN analysis of 16S rRNA gene sequence results, demographic and extensive questionnaire data, we describe both novel and previously documented associations between habits of women and their vaginal microbiome. The BN analysis approach shows promise in uncovering complex associations between disparate data types. Our findings based on this approach support published associations between specific microbiome members (e.g., Eggerthella, Gardnerella, Dialister, Sneathia and Ruminococcaceae, the Nugent score (a BV diagnostic and vaginal pH (a risk symptom of BV. Additionally, we found that several microbiome members were directly connected to other risk symptoms of BV (such as vaginal discharge, odor, itch, irritation, and yeast infection including L. jensenii, Corynebacteria, and Proteobacteria. No direct connections were found between the Nugent Score and risk symptoms of BV other than pH, indicating that the Nugent Score may not be the most useful criteria for assessment of clinical BV. We also found that demographics (i.e., age, ethnicity, previous pregnancy were associated with the presence/absence of specific vaginal microbes. The resulting BN revealed several as-yet undocumented associations between birth control usage, menstrual hygiene practices and specific microbiome members. Many of these complex relationships were not identified using common analytical methods, i.e., ordination and PERMANOVA. While these associations require confirmatory follow-up study, our findings strongly

  5. Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

    Science.gov (United States)

    Kunkle, Brian W.; Yoo, Changwon; Roy, Deodutta

    2013-01-01

    In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors. PMID:23737970

  6. Associations between sexual habits, menstrual hygiene practices, demographics and the vaginal microbiome as revealed by Bayesian network analysis.

    Science.gov (United States)

    Noyes, Noelle; Cho, Kyu-Chul; Ravel, Jacques; Forney, Larry J; Abdo, Zaid

    2018-01-01

    The vaginal microbiome plays an influential role in several disease states in reproductive age women, including bacterial vaginosis (BV). While demographic characteristics are associated with differences in vaginal microbiome community structure, little is known about the influence of sexual and hygiene habits. Furthermore, associations between the vaginal microbiome and risk symptoms of bacterial vaginosis have not been fully elucidated. Using Bayesian network (BN) analysis of 16S rRNA gene sequence results, demographic and extensive questionnaire data, we describe both novel and previously documented associations between habits of women and their vaginal microbiome. The BN analysis approach shows promise in uncovering complex associations between disparate data types. Our findings based on this approach support published associations between specific microbiome members (e.g., Eggerthella, Gardnerella, Dialister, Sneathia and Ruminococcaceae), the Nugent score (a BV diagnostic) and vaginal pH (a risk symptom of BV). Additionally, we found that several microbiome members were directly connected to other risk symptoms of BV (such as vaginal discharge, odor, itch, irritation, and yeast infection) including L. jensenii, Corynebacteria, and Proteobacteria. No direct connections were found between the Nugent Score and risk symptoms of BV other than pH, indicating that the Nugent Score may not be the most useful criteria for assessment of clinical BV. We also found that demographics (i.e., age, ethnicity, previous pregnancy) were associated with the presence/absence of specific vaginal microbes. The resulting BN revealed several as-yet undocumented associations between birth control usage, menstrual hygiene practices and specific microbiome members. Many of these complex relationships were not identified using common analytical methods, i.e., ordination and PERMANOVA. While these associations require confirmatory follow-up study, our findings strongly suggest that future

  7. Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator

    Science.gov (United States)

    Chang, Eugene T Y; Strong, Mark; Clayton, Richard H

    2015-01-01

    Models of electrical activity in cardiac cells have become important research tools as they can provide a quantitative description of detailed and integrative physiology. However, cardiac cell models have many parameters, and how uncertainties in these parameters affect the model output is difficult to assess without undertaking large numbers of model runs. In this study we show that a surrogate statistical model of a cardiac cell model (the Luo-Rudy 1991 model) can be built using Gaussian process (GP) emulators. Using this approach we examined how eight outputs describing the action potential shape and action potential duration restitution depend on six inputs, which we selected to be the maximum conductances in the Luo-Rudy 1991 model. We found that the GP emulators could be fitted to a small number of model runs, and behaved as would be expected based on the underlying physiology that the model represents. We have shown that an emulator approach is a powerful tool for uncertainty and sensitivity analysis in cardiac cell models. PMID:26114610

  8. Topographic factor analysis: a Bayesian model for inferring brain networks from neural data.

    Directory of Open Access Journals (Sweden)

    Jeremy R Manning

    Full Text Available The neural patterns recorded during a neuroscientific experiment reflect complex interactions between many brain regions, each comprising millions of neurons. However, the measurements themselves are typically abstracted from that underlying structure. For example, functional magnetic resonance imaging (fMRI datasets comprise a time series of three-dimensional images, where each voxel in an image (roughly reflects the activity of the brain structure(s-located at the corresponding point in space-at the time the image was collected. FMRI data often exhibit strong spatial correlations, whereby nearby voxels behave similarly over time as the underlying brain structure modulates its activity. Here we develop topographic factor analysis (TFA, a technique that exploits spatial correlations in fMRI data to recover the underlying structure that the images reflect. Specifically, TFA casts each brain image as a weighted sum of spatial functions. The parameters of those spatial functions, which may be learned by applying TFA to an fMRI dataset, reveal the locations and sizes of the brain structures activated while the data were collected, as well as the interactions between those structures.

  9. Bayesian models: A statistical primer for ecologists

    Science.gov (United States)

    Hobbs, N. Thompson; Hooten, Mevin B.

    2015-01-01

    Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

  10. A Bayesian Approach to Integrate Real-Time Data into Probabilistic Risk Analysis of Remediation Efforts in NAPL Sites

    Science.gov (United States)

    Fernandez-Garcia, D.; Sanchez-Vila, X.; Bolster, D.; Tartakovsky, D. M.

    2010-12-01

    The release of non-aqueous phase liquids (NAPLs) such as petroleum hydrocarbons and chlorinated solvents in the subsurface is a severe source of groundwater and vapor contamination. Because these liquids are essentially immiscible due to low solubility, these contaminants get slowly dissolved in groundwater and/or volatilized in the vadoze zone threatening the environment and public health over a long period. Many remediation technologies and strategies have been developed in the last decades for restoring the water quality properties of these contaminated sites. The failure of an on-site treatment technology application is often due to the unnoticed presence of dissolved NAPL entrapped in low permeability areas (heterogeneity) and/or the remaining of substantial amounts of pure phase after remediation efforts. Full understanding of the impact of remediation efforts is complicated due to the role of many interlink physical and biochemical processes taking place through several potential pathways of exposure to multiple receptors in a highly unknown heterogeneous environment. Due to these difficulties, the design of remediation strategies and definition of remediation endpoints have been traditionally determined without quantifying the risk associated with the failure of such efforts. We conduct a probabilistic risk analysis (PRA) of the likelihood of success of an on-site NAPL treatment technology that easily integrates all aspects of the problem (causes, pathways, and receptors) without doing extensive modeling. Importantly, the method is further capable to incorporate the inherent uncertainty that often exist in the exact location where the dissolved NAPL plume leaves the source zone. This is achieved by describing the failure of the system as a function of this source zone exit location, parameterized in terms of a vector of parameters. Using a Bayesian interpretation of the system and by means of the posterior multivariate distribution, the failure of the

  11. Approximate Bayesian computation.

    Directory of Open Access Journals (Sweden)

    Mikael Sunnåker

    Full Text Available Approximate Bayesian computation (ABC constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology.

  12. "A Bayesian sensitivity analysis to evaluate the impact of unmeasured confounding with external data: a real world comparative effectiveness study in osteoporosis".

    Science.gov (United States)

    Zhang, Xiang; Faries, Douglas E; Boytsov, Natalie; Stamey, James D; Seaman, John W

    2016-09-01

    Observational studies are frequently used to assess the effectiveness of medical interventions in routine clinical practice. However, the use of observational data for comparative effectiveness is challenged by selection bias and the potential of unmeasured confounding. This is especially problematic for analyses using a health care administrative database, in which key clinical measures are often not available. This paper provides an approach to conducting a sensitivity analyses to investigate the impact of unmeasured confounding in observational studies. In a real world osteoporosis comparative effectiveness study, the bone mineral density (BMD) score, an important predictor of fracture risk and a factor in the selection of osteoporosis treatments, is unavailable in the data base and lack of baseline BMD could potentially lead to significant selection bias. We implemented Bayesian twin-regression models, which simultaneously model both the observed outcome and the unobserved unmeasured confounder, using information from external sources. A sensitivity analysis was also conducted to assess the robustness of our conclusions to changes in such external data. The use of Bayesian modeling in this study suggests that the lack of baseline BMD did have a strong impact on the analysis, reversing the direction of the estimated effect (odds ratio of fracture incidence at 24 months: 0.40 vs. 1.36, with/without adjusting for unmeasured baseline BMD). The Bayesian twin-regression models provide a flexible sensitivity analysis tool to quantitatively assess the impact of unmeasured confounding in observational studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  13. Identifying the source of farmed escaped Atlantic salmon (Salmo salar): Bayesian clustering analysis increases accuracy of assignment

    DEFF Research Database (Denmark)

    Glover, Kevin A.; Hansen, Michael Møller; Skaala, Oystein

    2009-01-01

    Farmed Atlantic salmon escapees represent a significant threat to the genetic integrity of natural populations. Not all escapement events are reported, and consequently, there is a need to develop an effective tool for the identification of escapees. In this study, > 2200 salmon were collected from...... 44 cages located on 26 farms in the Hardangerfjord, western Norway. This fjord represents one of the major salmon farming areas in Norway, with a production of 57,000 t in 2007. Based upon genetic data from 17 microsatellite markers, significant but highly variable differentiation was observed among...... the 44 samples (cages), with pair-wise FST values ranging between 0.000 and 0.185. Bayesian clustering of the samples revealed five major genetic groups, into which the 44 samples were re-organised. Bayesian clustering also identified two samples consisting of fish with mixed genetic background...

  14. A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction

    KAUST Repository

    Ershadi, Ali

    2013-05-01

    The influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model. The Bayesian approach allows for an explicit quantification of the uncertainties in input variables: a source of error generally ignored in surface heat flux estimation. An application using field measurements from the Soil Moisture Experiment 2002 is presented. The spatial variability of selected input meteorological variables in a multitower site is used to formulate the prior estimates for the sampling uncertainties, and the likelihood function is formulated assuming Gaussian errors in the SEBS model. Land surface temperature, air temperature, and wind speed were estimated by sampling their posterior distribution using a Markov chain Monte Carlo algorithm. Results verify that Bayesian-inferred air temperature and wind speed were generally consistent with those observed at the towers, suggesting that local observations of these variables were spatially representative. Uncertainties in the land surface temperature appear to have the strongest effect on the estimated sensible heat flux, with Bayesian-inferred values differing by up to ±5°C from the observed data. These differences suggest that the footprint of the in situ measured land surface temperature is not representative of the larger-scale variability. As such, these measurements should be used with caution in the calculation of surface heat fluxes and highlight the importance of capturing the spatial variability in the land surface temperature: particularly, for remote sensing retrieval algorithms that use this variable for flux estimation.

  15. Bayesian structural equation modeling in sport and exercise psychology.

    Science.gov (United States)

    Stenling, Andreas; Ivarsson, Andreas; Johnson, Urban; Lindwall, Magnus

    2015-08-01

    Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.

  16. Comparing definitions of spatial relations for the analysis of geographic disparities in mortality within a Bayesian mixed-effects framework

    Directory of Open Access Journals (Sweden)

    Diego Fernando Rojas-Gualdrón

    Full Text Available ABSTRACT: Objective: To analyze the conceptual and technical differences between three definitions of spatial relations within a Bayesian mixed-effects framework: classical multilevel definition, spatial multiple membership definition and conditional autoregressive definition with an illustration of the estimate of geographic disparities in early neonatal mortality in Colombia, 2011-2014. Methods: A registry based cross-sectional study was conducted. Births and early neonatal deaths were obtained from the Colombian vital statistics registry for 2011-2014. Crude and adjusted Bayesian mixed effects regressions were performed for each definition of spatial relation. Model fit statistics, spatial autocorrelation of residuals and estimated mortality rates, geographic disparity measures, relative ratios and relative differences were compared. Results: The definition of spatial relations between municipalities based on the conditional autoregressive prior showed the best performance according to both fit statistics and residual spatial pattern analyses. Spatial multiple membership definition had a poor performance. Conclusion: Bayesian mixed effects regression with conditional autoregressive prior as an analytical framework may be an important contribution to epidemiological design as an improved alternative to ecological methods in the analyses of geographic disparities of mortality, considering potential ecological bias and spatial model misspecification.

  17. Comparing definitions of spatial relations for the analysis of geographic disparities in mortality within a Bayesian mixed-effects framework.

    Science.gov (United States)

    Rojas-Gualdrón, Diego Fernando

    2017-01-01

    To analyze the conceptual and technical differences between three definitions of spatial relations within a Bayesian mixed-effects framework: classical multilevel definition, spatial multiple membership definition and conditional autoregressive definition with an illustration of the estimate of geographic disparities in early neonatal mortality in Colombia, 2011-2014. A registry based cross-sectional study was conducted. Births and early neonatal deaths were obtained from the Colombian vital statistics registry for 2011-2014. Crude and adjusted Bayesian mixed effects regressions were performed for each definition of spatial relation. Model fit statistics, spatial autocorrelation of residuals and estimated mortality rates, geographic disparity measures, relative ratios and relative differences were compared. The definition of spatial relations between municipalities based on the conditional autoregressive prior showed the best performance according to both fit statistics and residual spatial pattern analyses. Spatial multiple membership definition had a poor performance. Bayesian mixed effects regression with conditional autoregressive prior as an analytical framework may be an important contribution to epidemiological design as an improved alternative to ecological methods in the analyses of geographic disparities of mortality, considering potential ecological bias and spatial model misspecification.

  18. Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT.

    Science.gov (United States)

    Bouhrara, Mustapha; Spencer, Richard G

    2016-02-15

    Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in the human brain. However, even for the simplest two-pool signal model consisting of myelin-associated and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNRs), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high-dimensional nature of the mcDESPOT signal model, and, therefore the high-dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of the MWF, the Bayesian analyses introduced here use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated markedly improved accuracy and precision in the estimation of MWF

  19. Using real-time PCR and Bayesian analysis to distinguish susceptible tubificid taxa important in the transmission of Myxobolus cerebralis, the cause of salmonid whirling disease.

    Science.gov (United States)

    Fytilis, Nikolaos; Rizzo, Donna M; Lamb, Ryan D; Kerans, Billie L; Stevens, Lori

    2013-05-01

    Aquatic oligochaetes have long been appreciated for their value in assessing habitat quality because they are ubiquitous sediment-dwelling filter feeders. Many oligochaete taxa are also important in the transmission of fish diseases. Distinguishing resistant and susceptible taxa is important for managing fish disease, yet challenging in practice. Tubifex tubifex (Oligochaeta: Tubificidae) is the definitive host for the complex life-cycle parasite, Myxobolus cerebralis, the causative agent of salmonid whirling disease. We developed two hydrolysis probe-based qualitative real-time PCR (qPCR) multiplex assays that distinguish among tubificid taxa collected from the Madison River, Montana, USA. The first assay distinguishes T. tubifex from Rhyacodrilus spp.; while the second classifies T. tubifex identified by the first assay into two genetic lineages (I and III). Specificity and sensitivity were optimized for each assay; the two assays showed specificity of 94.3% and 98.6% for the target oligochaetes, respectively. DNA sequencing verified the results. The development of these assays allowed us to more fully describe tubificid community composition (the taxa and their abundance at a site) and estimate the relative abundances of host taxa. To relate tubificid relative abundance to fish disease risk, we determined M. cerebralis infection prevalence in samples identified as T. tubifex using similar molecular techniques. Given prior information (i.e., morphological identification of sexually mature worms), Bayesian analysis inferred that the first qPCR assay improved taxonomic identification. Bayesian inference of the relative abundance of T. tubifex, combined with infection assay results, identified sites with a high prevalence of infected T. tubifex. To our knowledge, this study represents both the first assessment of oligochaete community composition using a qPCR assay based on fluorescent probes and the first use of Bayesian analysis to fully characterize the dominant

  20. Understanding Computational Bayesian Statistics

    CERN Document Server

    Bolstad, William M

    2011-01-01

    A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic