A Bayesian nonparametric approach to causal inference on quantiles.

Science.gov (United States)

Xu, Dandan; Daniels, Michael J; Winterstein, Almut G

2018-02-25

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.

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

MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH

Directory of Open Access Journals (Sweden)

D. Tutberidze

2017-04-01

Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.

A Bayesian approach to model uncertainty

International Nuclear Information System (INIS)

Buslik, A.

1994-01-01

A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given

Species trees from consensus single nucleotide polymorphism (SNP) data: Testing phylogenetic approaches with simulated and empirical data.

Science.gov (United States)

Schmidt-Lebuhn, Alexander N; Aitken, Nicola C; Chuah, Aaron

2017-11-01

Datasets of hundreds or thousands of SNPs (Single Nucleotide Polymorphisms) from multiple individuals per species are increasingly used to study population structure, species delimitation and shallow phylogenetics. The principal software tool to infer species or population trees from SNP data is currently the BEAST template SNAPP which uses a Bayesian coalescent analysis. However, it is computationally extremely demanding and tolerates only small amounts of missing data. We used simulated and empirical SNPs from plants (Australian Craspedia, Asteraceae, and Pelargonium, Geraniaceae) to compare species trees produced (1) by SNAPP, (2) using SVD quartets, and (3) using Bayesian and parsimony analysis with several different approaches to summarising data from multiple samples into one set of traits per species. Our aims were to explore the impact of tree topology and missing data on the results, and to test which data summarising and analyses approaches would best approximate the results obtained from SNAPP for empirical data. SVD quartets retrieved the correct topology from simulated data, as did SNAPP except in the case of a very unbalanced phylogeny. Both methods failed to retrieve the correct topology when large amounts of data were missing. Bayesian analysis of species level summary data scoring the two alleles of each SNP as independent characters and parsimony analysis of data scoring each SNP as one character produced trees with branch length distributions closest to the true trees on which SNPs were simulated. For empirical data, Bayesian inference and Dollo parsimony analysis of data scored allele-wise produced phylogenies most congruent with the results of SNAPP. In the case of study groups divergent enough for missing data to be phylogenetically informative (because of additional mutations preventing amplification of genomic fragments or bioinformatic establishment of homology), scoring of SNP data as a presence/absence matrix irrespective of allele

MCMC for parameters estimation by bayesian approach

International Nuclear Information System (INIS)

Ait Saadi, H.; Ykhlef, F.; Guessoum, A.

2011-01-01

This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.

A Bayesian approach to extracting meaning from system behavior

Energy Technology Data Exchange (ETDEWEB)

Dress, W.B.

1998-08-01

The modeling relation and its reformulation to include the semiotic hierarchy is essential for the understanding, control, and successful re-creation of natural systems. This presentation will argue for a careful application of Rosen`s modeling relationship to the problems of intelligence and autonomy in natural and artificial systems. To this end, the authors discuss the essential need for a correct theory of induction, learning, and probability; and suggest that modern Bayesian probability theory, developed by Cox, Jaynes, and others, can adequately meet such demands, especially on the operational level of extracting meaning from observations. The methods of Bayesian and maximum Entropy parameter estimation have been applied to measurements of system observables to directly infer the underlying differential equations generating system behavior. This approach by-passes the usual method of parameter estimation based on assuming a functional form for the observable and then estimating the parameters that would lead to the particular observed behavior. The computational savings is great since only location parameters enter into the maximum-entropy calculations; this innovation finesses the need for nonlinear parameters altogether. Such an approach more directly extracts the semantics inherent in a given system by going to the root of system meaning as expressed by abstract form or shape, rather than in syntactic particulars, such as signal amplitude and phase. Examples will be shown how the form of a system can be followed while ignoring unnecessary details. In this sense, the authors are observing the meaning of the words rather than being concerned with their particular expression or language. For the present discussion, empirical models are embodied by the differential equations underlying, producing, or describing the behavior of a process as measured or tracked by a particular variable set--the observables. The a priori models are probability structures that

Comparison between Fisherian and Bayesian approach to ...

African Journals Online (AJOL)

... of its simplicity and optimality properties is normally used for two group cases. However, Bayesian approach is found to be better than Fisher's approach because of its low misclassification error rate. Keywords: variance-covariance matrices, centroids, prior probability, mahalanobis distance, probability of misclassification ...

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 no...

Probabilistic Damage Characterization Using the Computationally-Efficient Bayesian Approach

Science.gov (United States)

Warner, James E.; Hochhalter, Jacob D.

2016-01-01

This work presents a computationally-ecient approach for damage determination that quanti es uncertainty in the provided diagnosis. Given strain sensor data that are polluted with measurement errors, Bayesian inference is used to estimate the location, size, and orientation of damage. This approach uses Bayes' Theorem to combine any prior knowledge an analyst may have about the nature of the damage with information provided implicitly by the strain sensor data to form a posterior probability distribution over possible damage states. The unknown damage parameters are then estimated based on samples drawn numerically from this distribution using a Markov Chain Monte Carlo (MCMC) sampling algorithm. Several modi cations are made to the traditional Bayesian inference approach to provide signi cant computational speedup. First, an ecient surrogate model is constructed using sparse grid interpolation to replace a costly nite element model that must otherwise be evaluated for each sample drawn with MCMC. Next, the standard Bayesian posterior distribution is modi ed using a weighted likelihood formulation, which is shown to improve the convergence of the sampling process. Finally, a robust MCMC algorithm, Delayed Rejection Adaptive Metropolis (DRAM), is adopted to sample the probability distribution more eciently. Numerical examples demonstrate that the proposed framework e ectively provides damage estimates with uncertainty quanti cation and can yield orders of magnitude speedup over standard Bayesian approaches.

[Bayesian approach for the cost-effectiveness evaluation of healthcare technologies].

Science.gov (United States)

Berchialla, Paola; Gregori, Dario; Brunello, Franco; Veltri, Andrea; Petrinco, Michele; Pagano, Eva

2009-01-01

The development of Bayesian statistical methods for the assessment of the cost-effectiveness of health care technologies is reviewed. Although many studies adopt a frequentist approach, several authors have advocated the use of Bayesian methods in health economics. Emphasis has been placed on the advantages of the Bayesian approach, which include: (i) the ability to make more intuitive and meaningful inferences; (ii) the ability to tackle complex problems, such as allowing for the inclusion of patients who generate no cost, thanks to the availability of powerful computational algorithms; (iii) the importance of a full use of quantitative and structural prior information to produce realistic inferences. Much literature comparing the cost-effectiveness of two treatments is based on the incremental cost-effectiveness ratio. However, new methods are arising with the purpose of decision making. These methods are based on a net benefits approach. In the present context, the cost-effectiveness acceptability curves have been pointed out to be intrinsically Bayesian in their formulation. They plot the probability of a positive net benefit against the threshold cost of a unit increase in efficacy.A case study is presented in order to illustrate the Bayesian statistics in the cost-effectiveness analysis. Emphasis is placed on the cost-effectiveness acceptability curves. Advantages and disadvantages of the method described in this paper have been compared to frequentist methods and discussed.

A Bayesian approach to person perception.

Science.gov (United States)

Clifford, C W G; Mareschal, I; Otsuka, Y; Watson, T L

2015-11-01

Here we propose a Bayesian approach to person perception, outlining the theoretical position and a methodological framework for testing the predictions experimentally. We use the term person perception to refer not only to the perception of others' personal attributes such as age and sex but also to the perception of social signals such as direction of gaze and emotional expression. The Bayesian approach provides a formal description of the way in which our perception combines current sensory evidence with prior expectations about the structure of the environment. Such expectations can lead to unconscious biases in our perception that are particularly evident when sensory evidence is uncertain. We illustrate the ideas with reference to our recent studies on gaze perception which show that people have a bias to perceive the gaze of others as directed towards themselves. We also describe a potential application to the study of the perception of a person's sex, in which a bias towards perceiving males is typically observed. Copyright © 2015 Elsevier Inc. All rights reserved.

A computational Bayesian approach to dependency assessment in system reliability

International Nuclear Information System (INIS)

Yontay, Petek; Pan, Rong

2016-01-01

Due to the increasing complexity of engineered products, it is of great importance to develop a tool to assess reliability dependencies among components and systems under the uncertainty of system reliability structure. In this paper, a Bayesian network approach is proposed for evaluating the conditional probability of failure within a complex system, using a multilevel system configuration. Coupling with Bayesian inference, the posterior distributions of these conditional probabilities can be estimated by combining failure information and expert opinions at both system and component levels. Three data scenarios are considered in this study, and they demonstrate that, with the quantification of the stochastic relationship of reliability within a system, the dependency structure in system reliability can be gradually revealed by the data collected at different system levels. - Highlights: • A Bayesian network representation of system reliability is presented. • Bayesian inference methods for assessing dependencies in system reliability are developed. • Complete and incomplete data scenarios are discussed. • The proposed approach is able to integrate reliability information from multiple sources at multiple levels of the system.

Accurate phenotyping: Reconciling approaches through Bayesian model averaging.

Directory of Open Access Journals (Sweden)

Carla Chia-Ming Chen

Full Text Available Genetic research into complex diseases is frequently hindered by a lack of clear biomarkers for phenotype ascertainment. Phenotypes for such diseases are often identified on the basis of clinically defined criteria; however such criteria may not be suitable for understanding the genetic composition of the diseases. Various statistical approaches have been proposed for phenotype definition; however our previous studies have shown that differences in phenotypes estimated using different approaches have substantial impact on subsequent analyses. Instead of obtaining results based upon a single model, we propose a new method, using Bayesian model averaging to overcome problems associated with phenotype definition. Although Bayesian model averaging has been used in other fields of research, this is the first study that uses Bayesian model averaging to reconcile phenotypes obtained using multiple models. We illustrate the new method by applying it to simulated genetic and phenotypic data for Kofendred personality disorder-an imaginary disease with several sub-types. Two separate statistical methods were used to identify clusters of individuals with distinct phenotypes: latent class analysis and grade of membership. Bayesian model averaging was then used to combine the two clusterings for the purpose of subsequent linkage analyses. We found that causative genetic loci for the disease produced higher LOD scores using model averaging than under either individual model separately. We attribute this improvement to consolidation of the cores of phenotype clusters identified using each individual method.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

Science.gov (United States)

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

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

Modelling of population dynamics of red king crab using Bayesian approach

Directory of Open Access Journals (Sweden)

Bakanev Sergey ...

2012-10-01

Modeling population dynamics based on the Bayesian approach enables to successfully resolve the above issues. The integration of the data from various studies into a unified model based on Bayesian parameter estimation method provides a much more detailed description of the processes occurring in the population.

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

A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

DEFF Research Database (Denmark)

Thomsen, Nanna Isbak; Binning, Philip John; McKnight, Ursula S.

2016-01-01

the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found...... to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models...... that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert...

Bayesian accounts of covert selective attention: A tutorial review.

Science.gov (United States)

Vincent, Benjamin T

2015-05-01

Decision making and optimal observer models offer an important theoretical approach to the study of covert selective attention. While their probabilistic formulation allows quantitative comparison to human performance, the models can be complex and their insights are not always immediately apparent. Part 1 establishes the theoretical appeal of the Bayesian approach, and introduces the way in which probabilistic approaches can be applied to covert search paradigms. Part 2 presents novel formulations of Bayesian models of 4 important covert attention paradigms, illustrating optimal observer predictions over a range of experimental manipulations. Graphical model notation is used to present models in an accessible way and Supplementary Code is provided to help bridge the gap between model theory and practical implementation. Part 3 reviews a large body of empirical and modelling evidence showing that many experimental phenomena in the domain of covert selective attention are a set of by-products. These effects emerge as the result of observers conducting Bayesian inference with noisy sensory observations, prior expectations, and knowledge of the generative structure of the stimulus environment.

Bayesian log-periodic model for financial crashes

DEFF Research Database (Denmark)

Rodríguez-Caballero, Carlos Vladimir; Knapik, Oskar

2014-01-01

This paper introduces a Bayesian approach in econophysics literature about financial bubbles in order to estimate the most probable time for a financial crash to occur. To this end, we propose using noninformative prior distributions to obtain posterior distributions. Since these distributions...... cannot be performed analytically, we develop a Markov Chain Monte Carlo algorithm to draw from posterior distributions. We consider three Bayesian models that involve normal and Student’s t-distributions in the disturbances and an AR(1)-GARCH(1,1) structure only within the first case. In the empirical...... part of the study, we analyze a well-known example of financial bubble – the S&P 500 1987 crash – to show the usefulness of the three methods under consideration and crashes of Merval-94, Bovespa-97, IPCMX-94, Hang Seng-97 using the simplest method. The novelty of this research is that the Bayesian...

Bayesian approach for the reliability assessment of corroded interdependent pipe networks

International Nuclear Information System (INIS)

Ait Mokhtar, El Hassene; Chateauneuf, Alaa; Laggoune, Radouane

2016-01-01

Pipelines under corrosion are subject to various environment conditions, and consequently it becomes difficult to build realistic corrosion models. In the present work, a Bayesian methodology is proposed to allow for updating the corrosion model parameters according to the evolution of environmental conditions. For reliability assessment of dependent structures, Bayesian networks are used to provide interesting qualitative and quantitative description of the information in the system. The qualitative contribution lies in the modeling of complex system, composed by dependent pipelines, as a Bayesian network. The quantitative one lies in the evaluation of the dependencies between pipelines by the use of a new method for the generation of conditional probability tables. The effectiveness of Bayesian updating is illustrated through an application where the new reliability of degraded (corroded) pipe networks is assessed. - Highlights: • A methodology for Bayesian network modeling of pipe networks is proposed. • Bayesian approach based on Metropolis - Hastings algorithm is conducted for corrosion model updating. • The reliability of corroded pipe network is assessed by considering the interdependencies between the pipelines.

Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions

Science.gov (United States)

Fienen, Michael N.; D'Oria, Marco; Doherty, John E.; Hunt, Randall J.

2013-01-01

The application bgaPEST is a highly parameterized inversion software package implementing the Bayesian Geostatistical Approach in a framework compatible with the parameter estimation suite PEST. Highly parameterized inversion refers to cases in which parameters are distributed in space or time and are correlated with one another. The Bayesian aspect of bgaPEST is related to Bayesian probability theory in which prior information about parameters is formally revised on the basis of the calibration dataset used for the inversion. Conceptually, this approach formalizes the conditionality of estimated parameters on the specific data and model available. The geostatistical component of the method refers to the way in which prior information about the parameters is used. A geostatistical autocorrelation function is used to enforce structure on the parameters to avoid overfitting and unrealistic results. Bayesian Geostatistical Approach is designed to provide the smoothest solution that is consistent with the data. Optionally, users can specify a level of fit or estimate a balance between fit and model complexity informed by the data. Groundwater and surface-water applications are used as examples in this text, but the possible uses of bgaPEST extend to any distributed parameter applications.

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.

Science.gov (United States)

Jones, Matt; Love, Bradley C

2011-08-01

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls

Bayesian Mediation Analysis

OpenAIRE

Yuan, Ying; MacKinnon, David P.

2009-01-01

This article proposes Bayesian analysis of mediation effects. Compared to 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 mediation analysis, inference is straightforward and exact, which makes it appealing for studies with small samples. Third, the Bayesian approach is conceptua...

A Bayesian approach to particle identification in ALICE

Energy Technology Data Exchange (ETDEWEB)

Wilkinson, Jeremy [Physikalisches Institut, Ruprecht-Karls-Universitaet Heidelberg (Germany); Collaboration: ALICE-Collaboration

2016-07-01

Particle identification (PID) is one of the major strengths of the ALICE detector at the LHC, and provides essential insight into quark-gluon plasma formation in heavy-ion collisions. PID is most effective when complementary identification techniques (such as specific energy loss in the Time Projection Chamber, or flight times measured by the Time Of Flight detector) are combined, however with standard PID techniques it can be difficult to combine these signals, especially when detectors with non-Gaussian responses are used. Here, an alternative probabilistic PID approach based on Bayes' theorem will be presented. This method facilitates the combination of different detector technologies based on the combined probability of a particle type to produce the signals measured in various detectors. The Bayesian PID approach will be briefly outlined, and benchmark analyses will be presented for high-purity samples of pions, kaons, and protons, as well as for the two-pronged decay D{sup 0} → K{sup -}π{sup +}, comparing the performance of the standard PID approach with that of the Bayesian approach. Finally, prospects for measuring the Λ{sub c} baryon in the three-pronged decay channel Λ{sub c}{sup +} → pK{sup -}π{sup +} are presented.

Bayesian Group Bridge for Bi-level Variable Selection.

Science.gov (United States)

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

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

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.

How to practise Bayesian statistics outside the Bayesian church: What philosophy for Bayesian statistical modelling?

NARCIS (Netherlands)

Borsboom, D.; Haig, B.D.

2013-01-01

Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science

Comparison between the basic least squares and the Bayesian approach for elastic constants identification

Science.gov (United States)

Gogu, C.; Haftka, R.; LeRiche, R.; Molimard, J.; Vautrin, A.; Sankar, B.

2008-11-01

The basic formulation of the least squares method, based on the L2 norm of the misfit, is still widely used today for identifying elastic material properties from experimental data. An alternative statistical approach is the Bayesian method. We seek here situations with significant difference between the material properties found by the two methods. For a simple three bar truss example we illustrate three such situations in which the Bayesian approach leads to more accurate results: different magnitude of the measurements, different uncertainty in the measurements and correlation among measurements. When all three effects add up, the Bayesian approach can have a large advantage. We then compared the two methods for identification of elastic constants from plate vibration natural frequencies.

Kaolin Quality Prediction from Samples: A Bayesian Network Approach

International Nuclear Information System (INIS)

Rivas, T.; Taboada, J.; Ordonez, C.; Matias, J. M.

2009-01-01

We describe the results of an expert system applied to the evaluation of samples of kaolin for industrial use in paper or ceramic manufacture. Different machine learning techniques - classification trees, support vector machines and Bayesian networks - were applied with the aim of evaluating and comparing their interpretability and prediction capacities. The predictive capacity of these models for the samples analyzed was highly satisfactory, both for ceramic quality and paper quality. However, Bayesian networks generally proved to be the most useful technique for our study, as this approach combines good predictive capacity with excellent interpretability of the kaolin quality structure, as it graphically represents relationships between variables and facilitates what-if analyses.

Empirical Bayesian Geographical Mapping of Occupational Accidents among Iranian Workers.

Science.gov (United States)

Vahabi, Nasim; Kazemnejad, Anoshirvan; Datta, Somnath

2017-05-01

Work-related accidents are believed to be a serious preventable cause of mortality and disability worldwide. This study aimed to provide Bayesian geographical maps of occupational injury rates among workers insured by the Iranian Social Security Organization. The participants included all insured workers in the Iranian Social Security Organization database in 2012. One of the applications of the Bayesian approach called the Poisson-Gamma model was applied to estimate the relative risk of occupational accidents. Data analysis and mapping were performed using R 3.0.3, Open-Bugs 3.2.3 rev 1012 and ArcMap9.3. The majority of all 21,484 investigated occupational injury victims were male (98.3%) including 16,443 (76.5%) single workers aged 20 - 29 years. The accidents were more frequent in basic metal, electric, and non-electric machining jobs. About 0.4% (96) of work-related accidents led to death, 2.2% (457) led to disability (partial and total), 4.6% (980) led to fixed compensation, and 92.8% (19,951) of the injured victims recovered completely. The geographical maps of estimated relative risk of occupational accidents were also provided. The results showed that the highest estimations pertained to provinces which were mostly located along mountain chains, some of which are categorized as deprived provinces in Iran. The study revealed the need for further investigation of the role of economic and climatic factors in high risk areas. The application of geographical mapping together with statistical approaches can provide more accurate tools for policy makers to make better decisions in order to prevent and reduce the risks and adverse outcomes of work-related accidents.

A Bayesian approach to estimating variance components within a multivariate generalizability theory framework.

Science.gov (United States)

Jiang, Zhehan; Skorupski, William

2017-12-12

In many behavioral research areas, multivariate generalizability theory (mG theory) has been typically used to investigate the reliability of certain multidimensional assessments. However, traditional mG-theory estimation-namely, using frequentist approaches-has limits, leading researchers to fail to take full advantage of the information that mG theory can offer regarding the reliability of measurements. Alternatively, Bayesian methods provide more information than frequentist approaches can offer. This article presents instructional guidelines on how to implement mG-theory analyses in a Bayesian framework; in particular, BUGS code is presented to fit commonly seen designs from mG theory, including single-facet designs, two-facet crossed designs, and two-facet nested designs. In addition to concrete examples that are closely related to the selected designs and the corresponding BUGS code, a simulated dataset is provided to demonstrate the utility and advantages of the Bayesian approach. This article is intended to serve as a tutorial reference for applied researchers and methodologists conducting mG-theory studies.

A Bayesian sequential processor approach to spectroscopic portal system decisions

Energy Technology Data Exchange (ETDEWEB)

Sale, K; Candy, J; Breitfeller, E; Guidry, B; Manatt, D; Gosnell, T; Chambers, D

2007-07-31

The development of faster more reliable techniques to detect radioactive contraband in a portal type scenario is an extremely important problem especially in this era of constant terrorist threats. Towards this goal the development of a model-based, Bayesian sequential data processor for the detection problem is discussed. In the sequential processor each datum (detector energy deposit and pulse arrival time) is used to update the posterior probability distribution over the space of model parameters. The nature of the sequential processor approach is that a detection is produced as soon as it is statistically justified by the data rather than waiting for a fixed counting interval before any analysis is performed. In this paper the Bayesian model-based approach, physics and signal processing models and decision functions are discussed along with the first results of our research.

A Bayesian hierarchical approach to comparative audit for carotid surgery.

Science.gov (United States)

Kuhan, G; Marshall, E C; Abidia, A F; Chetter, I C; McCollum, P T

2002-12-01

the aim of this study was to illustrate how a Bayesian hierarchical modelling approach can aid the reliable comparison of outcome rates between surgeons. retrospective analysis of prospective and retrospective data. binary outcome data (death/stroke within 30 days), together with information on 15 possible risk factors specific for CEA were available on 836 CEAs performed by four vascular surgeons from 1992-99. The median patient age was 68 (range 38-86) years and 60% were men. the model was developed using the WinBUGS software. After adjusting for patient-level risk factors, a cross-validatory approach was adopted to identify "divergent" performance. A ranking exercise was also carried out. the overall observed 30-day stroke/death rate was 3.9% (33/836). The model found diabetes, stroke and heart disease to be significant risk factors. There was no significant difference between the predicted and observed outcome rates for any surgeon (Bayesian p -value>0.05). Each surgeon had a median rank of 3 with associated 95% CI 1.0-5.0, despite the variability of observed stroke/death rate from 2.9-4.4%. After risk adjustment, there was very little residual between-surgeon variability in outcome rate. Bayesian hierarchical models can help to accurately quantify the uncertainty associated with surgeons' performance and rank.

Bayesian approach to analyzing holograms of colloidal particles.

Science.gov (United States)

Dimiduk, Thomas G; Manoharan, Vinothan N

2016-10-17

We demonstrate a Bayesian approach to tracking and characterizing colloidal particles from in-line digital holograms. We model the formation of the hologram using Lorenz-Mie theory. We then use a tempered Markov-chain Monte Carlo method to sample the posterior probability distributions of the model parameters: particle position, size, and refractive index. Compared to least-squares fitting, our approach allows us to more easily incorporate prior information about the parameters and to obtain more accurate uncertainties, which are critical for both particle tracking and characterization experiments. Our approach also eliminates the need to supply accurate initial guesses for the parameters, so it requires little tuning.

Review of Reliability-Based Design Optimization Approach and Its Integration with Bayesian Method

Science.gov (United States)

Zhang, Xiangnan

2018-03-01

A lot of uncertain factors lie in practical engineering, such as external load environment, material property, geometrical shape, initial condition, boundary condition, etc. Reliability method measures the structural safety condition and determine the optimal design parameter combination based on the probabilistic theory. Reliability-based design optimization (RBDO) is the most commonly used approach to minimize the structural cost or other performance under uncertainty variables which combines the reliability theory and optimization. However, it cannot handle the various incomplete information. The Bayesian approach is utilized to incorporate this kind of incomplete information in its uncertainty quantification. In this paper, the RBDO approach and its integration with Bayesian method are introduced.

A Bayesian Belief Network Approach to Predict Damages Caused by Disturbance Agents

Directory of Open Access Journals (Sweden)

Alfred Radl

2017-12-01

Full Text Available In mountain forests of Central Europe, storm and snow breakage as well as bark beetles are the prevailing major disturbances. The complex interrelatedness between climate, disturbance agents, and forest management increases the need for an integrative approach explicitly addressing the multiple interactions between environmental changes, forest management, and disturbance agents to support forest resource managers in adaptive management. Empirical data with a comprehensive coverage for modelling the susceptibility of forests and the impact of disturbance agents are rare, thus making probabilistic models, based on expert knowledge, one of the few modelling approaches that are able to handle uncertainties due to the available information. Bayesian belief networks (BBNs are a kind of probabilistic graphical model that has become very popular to practitioners and scientists mainly due to considerations of risk and uncertainties. In this contribution, we present a development methodology to define and parameterize BBNs based on expert elicitation and approximation. We modelled storm and bark beetle disturbances agents, analyzed effects of the development methodology on model structure, and evaluated behavior with stand data from Norway spruce (Picea abies (L. Karst. forests in southern Austria. The high vulnerability of the case study area according to different disturbance agents makes it particularly suitable for testing the BBN model.

A Bayesian concept learning approach to crowdsourcing

DEFF Research Database (Denmark)

Viappiani, P.; Zilles, S.; Hamilton, H.J.

2011-01-01

techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing......We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation...

Empirical Phenomenology: A Qualitative Research Approach (The ...

African Journals Online (AJOL)

Empirical Phenomenology: A Qualitative Research Approach (The Cologne Seminars) ... and practical application of empirical phenomenology in social research. ... and considers its implications for qualitative methods such as interviewing ...

A multi-agent systems approach to distributed bayesian information fusion

NARCIS (Netherlands)

Pavlin, G.; de Oude, P.; Maris, M.; Nunnink, J.; Hood, T.

2010-01-01

This paper introduces design principles for modular Bayesian fusion systems which can (i) cope with large quantities of heterogeneous information and (ii) can adapt to changing constellations of information sources on the fly. The presented approach exploits the locality of relations in causal

A Nonparametric Bayesian Approach For Emission Tomography Reconstruction

International Nuclear Information System (INIS)

Barat, Eric; Dautremer, Thomas

2007-01-01

We introduce a PET reconstruction algorithm following a nonparametric Bayesian (NPB) approach. In contrast with Expectation Maximization (EM), the proposed technique does not rely on any space discretization. Namely, the activity distribution--normalized emission intensity of the spatial poisson process--is considered as a spatial probability density and observations are the projections of random emissions whose distribution has to be estimated. This approach is nonparametric in the sense that the quantity of interest belongs to the set of probability measures on R k (for reconstruction in k-dimensions) and it is Bayesian in the sense that we define a prior directly on this spatial measure. In this context, we propose to model the nonparametric probability density as an infinite mixture of multivariate normal distributions. As a prior for this mixture we consider a Dirichlet Process Mixture (DPM) with a Normal-Inverse Wishart (NIW) model as base distribution of the Dirichlet Process. As in EM-family reconstruction, we use a data augmentation scheme where the set of hidden variables are the emission locations for each observed line of response in the continuous object space. Thanks to the data augmentation, we propose a Markov Chain Monte Carlo (MCMC) algorithm (Gibbs sampler) which is able to generate draws from the posterior distribution of the spatial intensity. A difference with EM is that one step of the Gibbs sampler corresponds to the generation of emission locations while only the expected number of emissions per pixel/voxel is used in EM. Another key difference is that the estimated spatial intensity is a continuous function such that there is no need to compute a projection matrix. Finally, draws from the intensity posterior distribution allow the estimation of posterior functionnals like the variance or confidence intervals. Results are presented for simulated data based on a 2D brain phantom and compared to Bayesian MAP-EM

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…

Bayesian methods for hackers probabilistic programming and Bayesian inference

CERN Document Server

Davidson-Pilon, Cameron

2016-01-01

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...

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.

A Bayesian network approach to the database search problem in criminal proceedings

Science.gov (United States)

2012-01-01

Background The ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions

Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.

Science.gov (United States)

Patri, Jean-François; Diard, Julien; Perrier, Pascal

2015-12-01

The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.

Uncertainty plus prior equals rational bias: an intuitive Bayesian probability weighting function.

Science.gov (United States)

Fennell, John; Baddeley, Roland

2012-10-01

Empirical research has shown that when making choices based on probabilistic options, people behave as if they overestimate small probabilities, underestimate large probabilities, and treat positive and negative outcomes differently. These distortions have been modeled using a nonlinear probability weighting function, which is found in several nonexpected utility theories, including rank-dependent models and prospect theory; here, we propose a Bayesian approach to the probability weighting function and, with it, a psychological rationale. In the real world, uncertainty is ubiquitous and, accordingly, the optimal strategy is to combine probability statements with prior information using Bayes' rule. First, we show that any reasonable prior on probabilities leads to 2 of the observed effects; overweighting of low probabilities and underweighting of high probabilities. We then investigate 2 plausible kinds of priors: informative priors based on previous experience and uninformative priors of ignorance. Individually, these priors potentially lead to large problems of bias and inefficiency, respectively; however, when combined using Bayesian model comparison methods, both forms of prior can be applied adaptively, gaining the efficiency of empirical priors and the robustness of ignorance priors. We illustrate this for the simple case of generic good and bad options, using Internet blogs to estimate the relevant priors of inference. Given this combined ignorant/informative prior, the Bayesian probability weighting function is not only robust and efficient but also matches all of the major characteristics of the distortions found in empirical research. PsycINFO Database Record (c) 2012 APA, all rights reserved.

BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.

Science.gov (United States)

Khakabimamaghani, Sahand; Ester, Martin

2016-01-01

The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data.

Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach

Directory of Open Access Journals (Sweden)

Dirk Eilander

2014-01-01

Full Text Available Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.

A Bayesian approach for parameter estimation and prediction using a computationally intensive model

International Nuclear Information System (INIS)

Higdon, Dave; McDonnell, Jordan D; Schunck, Nicolas; Sarich, Jason; Wild, Stefan M

2015-01-01

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model η(θ), where θ denotes the uncertain, best input setting. Hence the statistical model is of the form y=η(θ)+ϵ, where ϵ accounts for measurement, and possibly other, error sources. When nonlinearity is present in η(⋅), the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model η(⋅). This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. We also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory. (paper)

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...

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

Science.gov (United States)

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric

SHORT-TERM SOLAR FLARE LEVEL PREDICTION USING A BAYESIAN NETWORK APPROACH

International Nuclear Information System (INIS)

Yu Daren; Huang Xin; Hu Qinghua; Zhou Rui; Wang Huaning; Cui Yanmei

2010-01-01

A Bayesian network approach for short-term solar flare level prediction has been proposed based on three sequences of photospheric magnetic field parameters extracted from Solar and Heliospheric Observatory/Michelson Doppler Imager longitudinal magnetograms. The magnetic measures, the maximum horizontal gradient, the length of neutral line, and the number of singular points do not have determinate relationships with solar flares, so the solar flare level prediction is considered as an uncertainty reasoning process modeled by the Bayesian network. The qualitative network structure which describes conditional independent relationships among magnetic field parameters and the quantitative conditional probability tables which determine the probabilistic values for each variable are learned from the data set. Seven sequential features-the maximum, the mean, the root mean square, the standard deviation, the shape factor, the crest factor, and the pulse factor-are extracted to reduce the dimensions of the raw sequences. Two Bayesian network models are built using raw sequential data (BN R ) and feature extracted data (BN F ), respectively. The explanations of these models are consistent with physical analyses of experts. The performances of the BN R and the BN F appear comparable with other methods. More importantly, the comprehensibility of the Bayesian network models is better than other methods.

Bayesian benefits with JASP

NARCIS (Netherlands)

Marsman, M.; Wagenmakers, E.-J.

2017-01-01

We illustrate the Bayesian approach to data analysis using the newly developed statistical software program JASP. With JASP, researchers are able to take advantage of the benefits that the Bayesian framework has to offer in terms of parameter estimation and hypothesis testing. The Bayesian

Bayesian-based estimation of acoustic surface impedance: Finite difference frequency domain approach.

Science.gov (United States)

Bockman, Alexander; Fackler, Cameron; Xiang, Ning

2015-04-01

Acoustic performance for an interior requires an accurate description of the boundary materials' surface acoustic impedance. Analytical methods may be applied to a small class of test geometries, but inverse numerical methods provide greater flexibility. The parameter estimation problem requires minimizing prediction vice observed acoustic field pressure. The Bayesian-network sampling approach presented here mitigates other methods' susceptibility to noise inherent to the experiment, model, and numerics. A geometry agnostic method is developed here and its parameter estimation performance is demonstrated for an air-backed micro-perforated panel in an impedance tube. Good agreement is found with predictions from the ISO standard two-microphone, impedance-tube method, and a theoretical model for the material. Data by-products exclusive to a Bayesian approach are analyzed to assess sensitivity of the method to nuisance parameters.

Capturing changes in flood risk with Bayesian approaches for flood damage assessment

Science.gov (United States)

Vogel, Kristin; Schröter, Kai; Kreibich, Heidi; Thieken, Annegret; Müller, Meike; Sieg, Tobias; Laudan, Jonas; Kienzler, Sarah; Weise, Laura; Merz, Bruno; Scherbaum, Frank

2016-04-01

Flood risk is a function of hazard as well as of exposure and vulnerability. All three components are under change over space and time and have to be considered for reliable damage estimations and risk analyses, since this is the basis for an efficient, adaptable risk management. Hitherto, models for estimating flood damage are comparatively simple and cannot sufficiently account for changing conditions. The Bayesian network approach allows for a multivariate modeling of complex systems without relying on expert knowledge about physical constraints. In a Bayesian network each model component is considered to be a random variable. The way of interactions between those variables can be learned from observations or be defined by expert knowledge. Even a combination of both is possible. Moreover, the probabilistic framework captures uncertainties related to the prediction and provides a probability distribution for the damage instead of a point estimate. The graphical representation of Bayesian networks helps to study the change of probabilities for changing circumstances and may thus simplify the communication between scientists and public authorities. In the framework of the DFG-Research Training Group "NatRiskChange" we aim to develop Bayesian networks for flood damage and vulnerability assessments of residential buildings and companies under changing conditions. A Bayesian network learned from data, collected over the last 15 years in flooded regions in the Elbe and Danube catchments (Germany), reveals the impact of many variables like building characteristics, precaution and warning situation on flood damage to residential buildings. While the handling of incomplete and hybrid (discrete mixed with continuous) data are the most challenging issues in the study on residential buildings, a similar study, that focuses on the vulnerability of small to medium sized companies, bears new challenges. Relying on a much smaller data set for the determination of the model

Pedestrian fatality and natural light: Evidence from South Africa using a Bayesian approach

CSIR Research Space (South Africa)

Das, Sonali

2014-02-01

Full Text Available from South Africa using a Bayesian approach, Econ.Model. (2013), http://dx.doi.org/10.1016/j.econmod.2013.11.037 U N C O R R E C TE D P R O O F 95 countries the most vulnerable group is the economically active sector 96 namely working adults... using a Bayesian approach, Econ.Model. (2013), http://dx.doi.org/10.1016/j.econmod.2013.11.037 U N C O R R E C TE D P R O O F 157 relates the chi-square and Poisson distributions (Johnson and Kotz, 158 1969; Stuart and Ord, 1994) is given by: l ¼ χ2a 2...

Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis.

Science.gov (United States)

Deetjen, Ulrike; Powell, John A

2016-05-01

This research examines the extent to which informational and emotional elements are employed in online support forums for 14 purposively sampled chronic medical conditions and the factors that influence whether posts are of a more informational or emotional nature. Large-scale qualitative data were obtained from Dailystrength.org. Based on a hand-coded training dataset, all posts were classified into informational or emotional using a Bayesian classification algorithm to generalize the findings. Posts that could not be classified with a probability of at least 75% were excluded. The overall tendency toward emotional posts differs by condition: mental health (depression, schizophrenia) and Alzheimer's disease consist of more emotional posts, while informational posts relate more to nonterminal physical conditions (irritable bowel syndrome, diabetes, asthma). There is no gender difference across conditions, although prostate cancer forums are oriented toward informational support, whereas breast cancer forums rather feature emotional support. Across diseases, the best predictors for emotional content are lower age and a higher number of overall posts by the support group member. The results are in line with previous empirical research and unify empirical findings from single/2-condition research. Limitations include the analytical restriction to predefined categories (informational, emotional) through the chosen machine-learning approach. Our findings provide an empirical foundation for building theory on informational versus emotional support across conditions, give insights for practitioners to better understand the role of online support groups for different patients, and show the usefulness of machine-learning approaches to analyze large-scale qualitative health data from online settings. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Life cycle reliability assessment of new products—A Bayesian model updating approach

International Nuclear Information System (INIS)

Peng, Weiwen; Huang, Hong-Zhong; Li, Yanfeng; Zuo, Ming J.; Xie, Min

2013-01-01

The rapidly increasing pace and continuously evolving reliability requirements of new products have made life cycle reliability assessment of new products an imperative yet difficult work. While much work has been done to separately estimate reliability of new products in specific stages, a gap exists in carrying out life cycle reliability assessment throughout all life cycle stages. We present a Bayesian model updating approach (BMUA) for life cycle reliability assessment of new products. Novel features of this approach are the development of Bayesian information toolkits by separately including “reliability improvement factor” and “information fusion factor”, which allow the integration of subjective information in a specific life cycle stage and the transition of integrated information between adjacent life cycle stages. They lead to the unique characteristics of the BMUA in which information generated throughout life cycle stages are integrated coherently. To illustrate the approach, an application to the life cycle reliability assessment of a newly developed Gantry Machining Center is shown

Bayesian approaches for detecting significant deterioration

International Nuclear Information System (INIS)

Roed, Willy; Aven, Terje

2009-01-01

Risk indicators can provide useful input to risk management processes and are given increased attention in the Norwegian petroleum industry. Examples include indicators expressing the proportion of test failures of safety and barrier systems. Such indicators give valuable information about the performance of the systems and provide a basis for trend evaluations. Early warning of a possible deterioration is essential due to the importance of the systems in focus, but what should be the basis for the warning criterion? This paper presents and discusses several Bayesian approaches for the establishment of a warning criterion to disclose significant deterioration. The Norwegian petroleum industry is the starting point for this paper, but the study is relevant for other application areas as well

A Bayesian approach to estimate sensible and latent heat over vegetated land surface

Directory of Open Access Journals (Sweden)

C. van der Tol

2009-06-01

Full Text Available Sensible and latent heat fluxes are often calculated from bulk transfer equations combined with the energy balance. For spatial estimates of these fluxes, a combination of remotely sensed and standard meteorological data from weather stations is used. The success of this approach depends on the accuracy of the input data and on the accuracy of two variables in particular: aerodynamic and surface conductance. This paper presents a Bayesian approach to improve estimates of sensible and latent heat fluxes by using a priori estimates of aerodynamic and surface conductance alongside remote measurements of surface temperature. The method is validated for time series of half-hourly measurements in a fully grown maize field, a vineyard and a forest. It is shown that the Bayesian approach yields more accurate estimates of sensible and latent heat flux than traditional methods.

Upper limit for Poisson variable incorporating systematic uncertainties by Bayesian approach

International Nuclear Information System (INIS)

Zhu, Yongsheng

2007-01-01

To calculate the upper limit for the Poisson observable at given confidence level with inclusion of systematic uncertainties in background expectation and signal efficiency, formulations have been established along the line of Bayesian approach. A FORTRAN program, BPULE, has been developed to implement the upper limit calculation

Air kerma rate estimation by means of in-situ gamma spectrometry: A Bayesian approach

International Nuclear Information System (INIS)

Cabal, Gonzalo; Kluson, Jaroslav

2008-01-01

Full text: Bayesian inference is used to determine the Air Kerma Rate based on a set of in situ environmental gamma spectra measurements performed with a NaI(Tl) scintillation detector. A natural advantage of such approach is the possibility to quantify uncertainty not only in the Air Kerma Rate estimation but also for the gamma spectra which is unfolded within the procedure. The measurements were performed using a 3'' x 3'' NaI(Tl) scintillation detector. The response matrices of such detection system were calculated using a Monte Carlo code. For the calculations of the spectra as well as the Air Kerma Rate the WinBugs program was used. WinBugs is a dedicated software for Bayesian inference using Monte Carlo Markov chain methods (MCMC). The results of such calculations are shown and compared with other non-Bayesian approachs such as the Scofield-Gold iterative method and the Maximum Entropy Method

Bayesian Multi-Energy Computed Tomography reconstruction approaches based on decomposition models

International Nuclear Information System (INIS)

Cai, Caifang

2013-01-01

Multi-Energy Computed Tomography (MECT) makes it possible to get multiple fractions of basis materials without segmentation. In medical application, one is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical MECT measurements are usually obtained with polychromatic X-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam poly-chromaticity fail to estimate the correct decomposition fractions and result in Beam-Hardening Artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log pre-processing and the water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on non-linear forward models counting the beam poly-chromaticity show great potential for giving accurate fraction images.This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint Maximum A Posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a non-quadratic cost function. To solve it, the use of a monotone Conjugate Gradient (CG) algorithm with suboptimal descent steps is proposed.The performances of the proposed approach are analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also

Estimation of the order of an autoregressive time series: a Bayesian approach

International Nuclear Information System (INIS)

Robb, L.J.

1980-01-01

Finite-order autoregressive models for time series are often used for prediction and other inferences. Given the order of the model, the parameters of the models can be estimated by least-squares, maximum-likelihood, or Yule-Walker method. The basic problem is estimating the order of the model. The problem of autoregressive order estimation is placed in a Bayesian framework. This approach illustrates how the Bayesian method brings the numerous aspects of the problem together into a coherent structure. A joint prior probability density is proposed for the order, the partial autocorrelation coefficients, and the variance; and the marginal posterior probability distribution for the order, given the data, is obtained. It is noted that the value with maximum posterior probability is the Bayes estimate of the order with respect to a particular loss function. The asymptotic posterior distribution of the order is also given. In conclusion, Wolfer's sunspot data as well as simulated data corresponding to several autoregressive models are analyzed according to Akaike's method and the Bayesian method. Both methods are observed to perform quite well, although the Bayesian method was clearly superior, in most cases

Merging Digital Surface Models Implementing Bayesian Approaches

Science.gov (United States)

Sadeq, H.; Drummond, J.; Li, Z.

2016-06-01

In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.

MERGING DIGITAL SURFACE MODELS IMPLEMENTING BAYESIAN APPROACHES

Directory of Open Access Journals (Sweden)

H. Sadeq

2016-06-01

Full Text Available In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades. It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.

A Decomposition Algorithm for Learning Bayesian Network Structures from Data

DEFF Research Database (Denmark)

Zeng, Yifeng; Cordero Hernandez, Jorge

2008-01-01

It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

A Bayesian Approach for Sensor Optimisation in Impact Identification

Directory of Open Access Journals (Sweden)

Vincenzo Mallardo

2016-11-01

Full Text Available This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating impacts on composite structures. A Bayesian-based objective function is adopted in the optimisation procedure as an indicator of the performance of meta-models developed for different sensor combinations to locate various impact events. To represent a real structure under operational load and to increase the reliability of the Structural Health Monitoring (SHM system, the probability of malfunctioning sensors is included in the optimisation. The reliability and the robustness of the procedure is tested with experimental and numerical examples. Finally, the proposed optimisation algorithm is applied to a composite stiffened panel for both the uniform and non-uniform probability of impact occurrence.

A Robust Bayesian Approach for Structural Equation Models with Missing Data

Science.gov (United States)

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison.

Science.gov (United States)

Cooper, Richard J; Krueger, Tobias; Hiscock, Kevin M; Rawlins, Barry G

2014-11-01

Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations. An OFAT sensitivity analysis of sediment fingerprinting mixing models is conductedBayesian models display high sensitivity to error assumptions and structural choicesSource apportionment results differ between Bayesian and frequentist approaches.

Bayesian Reliability Estimation for Deteriorating Systems with Limited Samples Using the Maximum Entropy Approach

OpenAIRE

Xiao, Ning-Cong; Li, Yan-Feng; Wang, Zhonglai; Peng, Weiwen; Huang, Hong-Zhong

2013-01-01

In this paper the combinations of maximum entropy method and Bayesian inference for reliability assessment of deteriorating system is proposed. Due to various uncertainties, less data and incomplete information, system parameters usually cannot be determined precisely. These uncertainty parameters can be modeled by fuzzy sets theory and the Bayesian inference which have been proved to be useful for deteriorating systems under small sample sizes. The maximum entropy approach can be used to cal...

A new method for E-government procurement using collaborative filtering and Bayesian approach.

Science.gov (United States)

Zhang, Shuai; Xi, Chengyu; Wang, Yan; Zhang, Wenyu; Chen, Yanhong

2013-01-01

Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.

A New Method for E-Government Procurement Using Collaborative Filtering and Bayesian Approach

Directory of Open Access Journals (Sweden)

Shuai Zhang

2013-01-01

Full Text Available Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP to search for the optimal procurement scheme (OPS. Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services’ attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.

BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

Directory of Open Access Journals (Sweden)

Mihaela Simionescu

2014-12-01

Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

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...

A nonparametric Bayesian approach for genetic evaluation in ...

African Journals Online (AJOL)

South African Journal of Animal Science ... the Bayesian and Classical models, a Bayesian procedure is provided which allows these random ... data from the Elsenburg Dormer sheep stud and data from a simulation experiment are utilized. >

Application of Bayesian approach to estimate average level spacing

International Nuclear Information System (INIS)

Huang Zhongfu; Zhao Zhixiang

1991-01-01

A method to estimate average level spacing from a set of resolved resonance parameters by using Bayesian approach is given. Using the information given in the distributions of both levels spacing and neutron width, the level missing in measured sample can be corrected more precisely so that better estimate for average level spacing can be obtained by this method. The calculation of s-wave resonance has been done and comparison with other work was carried out

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…

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.

[Overcoming the limitations of the descriptive and categorical approaches in psychiatric diagnosis: a proposal based on Bayesian networks].

Science.gov (United States)

Sorias, Soli

2015-01-01

Efforts to overcome the problems of descriptive and categorical approaches have not yielded results. In the present article, psychiatric diagnosis using Bayesian networks is proposed. Instead of a yes/no decision, Bayesian networks give the probability of diagnostic category inclusion, thereby yielding both a graded, i.e., dimensional diagnosis, and a value of the certainty of the diagnosis. With the use of Bayesian networks in the diagnosis of mental disorders, information about etiology, associated features, treatment outcome, and laboratory results may be used in addition to clinical signs and symptoms, with each of these factors contributing proportionally to their own specificity and sensitivity. Furthermore, a diagnosis (albeit one with a lower probability) can be made even with incomplete, uncertain, or partially erroneous information, and patients whose symptoms are below the diagnostic threshold can be evaluated. Lastly, there is no need of NOS or "unspecified" categories, and comorbid disorders become different dimensions of the diagnostic evaluation. Bayesian diagnoses allow the preservation of current categories and assessment methods, and may be used concurrently with criteria-based diagnoses. Users need not put in extra effort except to collect more comprehensive information. Unlike the Research Domain Criteria (RDoC) project, the Bayesian approach neither increases the diagnostic validity of existing categories nor explains the pathophysiological mechanisms of mental disorders. It, however, can be readily integrated to present classification systems. Therefore, the Bayesian approach may be an intermediate phase between criteria-based diagnosis and the RDoC ideal.

A Bayesian Approach to Real-Time Earthquake Phase Association

Science.gov (United States)

Benz, H.; Johnson, C. E.; Earle, P. S.; Patton, J. M.

2014-12-01

Real-time location of seismic events requires a robust and extremely efficient means of associating and identifying seismic phases with hypothetical sources. An association algorithm converts a series of phase arrival times into a catalog of earthquake hypocenters. The classical approach based on time-space stacking of the locus of possible hypocenters for each phase arrival using the principal of acoustic reciprocity has been in use now for many years. One of the most significant problems that has emerged over time with this approach is related to the extreme variations in seismic station density throughout the global seismic network. To address this problem we have developed a novel, Bayesian association algorithm, which looks at the association problem as a dynamically evolving complex system of "many to many relationships". While the end result must be an array of one to many relations (one earthquake, many phases), during the association process the situation is quite different. Both the evolving possible hypocenters and the relationships between phases and all nascent hypocenters is many to many (many earthquakes, many phases). The computational framework we are using to address this is a responsive, NoSQL graph database where the earthquake-phase associations are represented as intersecting Bayesian Learning Networks. The approach directly addresses the network inhomogeneity issue while at the same time allowing the inclusion of other kinds of data (e.g., seismic beams, station noise characteristics, priors on estimated location of the seismic source) by representing the locus of intersecting hypothetical loci for a given datum as joint probability density functions.

A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites

Science.gov (United States)

Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

2009-05-01

Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

A non-parametric Bayesian approach to decompounding from high frequency data

NARCIS (Netherlands)

Gugushvili, Shota; van der Meulen, F.H.; Spreij, Peter

2016-01-01

Given a sample from a discretely observed compound Poisson process, we consider non-parametric estimation of the density f0 of its jump sizes, as well as of its intensity λ0. We take a Bayesian approach to the problem and specify the prior on f0 as the Dirichlet location mixture of normal densities.

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming

2009-02-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.

Science.gov (United States)

Houseman, E Andres; Virji, M Abbas

2017-08-01

Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest. A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance. Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates

Overlapping community detection in weighted networks via a Bayesian approach

Science.gov (United States)

Chen, Yi; Wang, Xiaolong; Xiang, Xin; Tang, Buzhou; Chen, Qingcai; Fan, Shixi; Bu, Junzhao

2017-02-01

Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

Quantitative Precipitation Estimation over Ocean Using Bayesian Approach from Microwave Observations during the Typhoon Season

Directory of Open Access Journals (Sweden)

Jen-Chi Hu

2009-01-01

Full Text Available We have developed a new Bayesian approach to retrieve oceanic rain rate from the Tropical Rainfall Measuring Mission (TRMM Microwave Imager (TMI, with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR, the Goddard Profiling Algorithm (GPROF, and a multi-channel linear regression statistical method (MLRS. We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS error against rain gauge data for 16 typhoon over passes in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and ~2 mm hr-1, respectively. In particular, at heavy rain rates, our Bayesian retrievals out perform those retrieved from GPROF and MLRS. Over all, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Ac cu rate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.

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.

GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach.

Science.gov (United States)

Zhang, Song; Cao, Jing; Kong, Y Megan; Scheuermann, Richard H

2010-04-01

A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy. We have developed a Bayesian approach (GO-Bayes) to measure overrepresentation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming; Zhang, Jian

2009-01-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly

Banking Fragility in Colombia: An Empirical Analysis Based on Balance Sheets

OpenAIRE

Ignacio Lozano; Alexander Guarín

2014-01-01

In this paper, we study the empirical relationship between credit funding sources and the financial vulnerability of the Colombian banking system. We propose a statistical model to measure and predict banking-fragility episodes associated with credit funding sources classified into retail deposits and wholesale funds. We compute the probability of financial fragility for both the aggregated banking system and the individual banks. Our approach performs a Bayesian averaging of estimated logit ...

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

Bayesian statistics an introduction

CERN Document Server

Lee, Peter M

2012-01-01

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel

Collective Labour Supply, Taxes, and Intrahousehold Allocation: An Empirical Approach

NARCIS (Netherlands)

Bloemen, H.G.

2017-01-01

Most empirical studies of the impact of labour income taxation on the labour supply behaviour of households use a unitary modelling approach. In this paper we empirically analyze income taxation and the choice of working hours by combining the collective approach for household behaviour and the

Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration.

Science.gov (United States)

Conner, Mary M; Saunders, W Carl; Bouwes, Nicolaas; Jordan, Chris

2015-10-01

Before-after-control-impact (BACI) designs are an effective method to evaluate natural and human-induced perturbations on ecological variables when treatment sites cannot be randomly chosen. While effect sizes of interest can be tested with frequentist methods, using Bayesian Markov chain Monte Carlo (MCMC) sampling methods, probabilities of effect sizes, such as a ≥20 % increase in density after restoration, can be directly estimated. Although BACI and Bayesian methods are used widely for assessing natural and human-induced impacts for field experiments, the application of hierarchal Bayesian modeling with MCMC sampling to BACI designs is less common. Here, we combine these approaches and extend the typical presentation of results with an easy to interpret ratio, which provides an answer to the main study question-"How much impact did a management action or natural perturbation have?" As an example of this approach, we evaluate the impact of a restoration project, which implemented beaver dam analogs, on survival and density of juvenile steelhead. Results indicated the probabilities of a ≥30 % increase were high for survival and density after the dams were installed, 0.88 and 0.99, respectively, while probabilities for a higher increase of ≥50 % were variable, 0.17 and 0.82, respectively. This approach demonstrates a useful extension of Bayesian methods that can easily be generalized to other study designs from simple (e.g., single factor ANOVA, paired t test) to more complicated block designs (e.g., crossover, split-plot). This approach is valuable for estimating the probabilities of restoration impacts or other management actions.

A Bayesian inference approach to unveil supply curves in electricity markets

DEFF Research Database (Denmark)

Mitridati, Lesia Marie-Jeanne Mariane; Pinson, Pierre

2017-01-01

in the literature on modeling this uncertainty. In this study we introduce a Bayesian inference approach to reveal the aggregate supply curve in a day-ahead electricity market. The proposed algorithm relies on Markov Chain Monte Carlo and Sequential Monte Carlo methods. The major appeal of this approach......With increased competition in wholesale electricity markets, the need for new decision-making tools for strategic producers has arisen. Optimal bidding strategies have traditionally been modeled as stochastic profit maximization problems. However, for producers with non-negligible market power...

Bayesian Dimensionality Assessment for the Multidimensional Nominal Response Model

Directory of Open Access Journals (Sweden)

Javier Revuelta

2017-06-01

Full Text Available This article introduces Bayesian estimation and evaluation procedures for the multidimensional nominal response model. The utility of this model is to perform a nominal factor analysis of items that consist of a finite number of unordered response categories. The key aspect of the model, in comparison with traditional factorial model, is that there is a slope for each response category on the latent dimensions, instead of having slopes associated to the items. The extended parameterization of the multidimensional nominal response model requires large samples for estimation. When sample size is of a moderate or small size, some of these parameters may be weakly empirically identifiable and the estimation algorithm may run into difficulties. We propose a Bayesian MCMC inferential algorithm to estimate the parameters and the number of dimensions underlying the multidimensional nominal response model. Two Bayesian approaches to model evaluation were compared: discrepancy statistics (DIC, WAICC, and LOO that provide an indication of the relative merit of different models, and the standardized generalized discrepancy measure that requires resampling data and is computationally more involved. A simulation study was conducted to compare these two approaches, and the results show that the standardized generalized discrepancy measure can be used to reliably estimate the dimensionality of the model whereas the discrepancy statistics are questionable. The paper also includes an example with real data in the context of learning styles, in which the model is used to conduct an exploratory factor analysis of nominal data.

A Bayesian approach for quantification of model uncertainty

International Nuclear Information System (INIS)

Park, Inseok; Amarchinta, Hemanth K.; Grandhi, Ramana V.

2010-01-01

In most engineering problems, more than one model can be created to represent an engineering system's behavior. Uncertainty is inevitably involved in selecting the best model from among the models that are possible. Uncertainty in model selection cannot be ignored, especially when the differences between the predictions of competing models are significant. In this research, a methodology is proposed to quantify model uncertainty using measured differences between experimental data and model outcomes under a Bayesian statistical framework. The adjustment factor approach is used to propagate model uncertainty into prediction of a system response. A nonlinear vibration system is used to demonstrate the processes for implementing the adjustment factor approach. Finally, the methodology is applied on the engineering benefits of a laser peening process, and a confidence band for residual stresses is established to indicate the reliability of model prediction.

Bayesian networks improve causal environmental ...

Science.gov (United States)

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Bayesian inference with ecological applications

CERN Document Server

Link, William A

2009-01-01

This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...

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.

Bayesian ensemble approach to error estimation of interatomic potentials

DEFF Research Database (Denmark)

Frederiksen, Søren Lund; Jacobsen, Karsten Wedel; Brown, K.S.

2004-01-01

Using a Bayesian approach a general method is developed to assess error bars on predictions made by models fitted to data. The error bars are estimated from fluctuations in ensembles of models sampling the model-parameter space with a probability density set by the minimum cost. The method...... is applied to the development of interatomic potentials for molybdenum using various potential forms and databases based on atomic forces. The calculated error bars on elastic constants, gamma-surface energies, structural energies, and dislocation properties are shown to provide realistic estimates...

Drug-target interaction prediction: A Bayesian ranking approach.

Science.gov (United States)

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method

International Nuclear Information System (INIS)

Ma Xiang; Zabaras, Nicholas

2009-01-01

A new approach to modeling inverse problems using a Bayesian inference method is introduced. The Bayesian approach considers the unknown parameters as random variables and seeks the probabilistic distribution of the unknowns. By introducing the concept of the stochastic prior state space to the Bayesian formulation, we reformulate the deterministic forward problem as a stochastic one. The adaptive hierarchical sparse grid collocation (ASGC) method is used for constructing an interpolant to the solution of the forward model in this prior space which is large enough to capture all the variability/uncertainty in the posterior distribution of the unknown parameters. This solution can be considered as a function of the random unknowns and serves as a stochastic surrogate model for the likelihood calculation. Hierarchical Bayesian formulation is used to derive the posterior probability density function (PPDF). The spatial model is represented as a convolution of a smooth kernel and a Markov random field. The state space of the PPDF is explored using Markov chain Monte Carlo algorithms to obtain statistics of the unknowns. The likelihood calculation is performed by directly sampling the approximate stochastic solution obtained through the ASGC method. The technique is assessed on two nonlinear inverse problems: source inversion and permeability estimation in flow through porous media

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.

Bayesian approach to estimate AUC, partition coefficient and drug targeting index for studies with serial sacrifice design.

Science.gov (United States)

Wang, Tianli; Baron, Kyle; Zhong, Wei; Brundage, Richard; Elmquist, William

2014-03-01

The current study presents a Bayesian approach to non-compartmental analysis (NCA), which provides the accurate and precise estimate of AUC 0 (∞) and any AUC 0 (∞) -based NCA parameter or derivation. In order to assess the performance of the proposed method, 1,000 simulated datasets were generated in different scenarios. A Bayesian method was used to estimate the tissue and plasma AUC 0 (∞) s and the tissue-to-plasma AUC 0 (∞) ratio. The posterior medians and the coverage of 95% credible intervals for the true parameter values were examined. The method was applied to laboratory data from a mice brain distribution study with serial sacrifice design for illustration. Bayesian NCA approach is accurate and precise in point estimation of the AUC 0 (∞) and the partition coefficient under a serial sacrifice design. It also provides a consistently good variance estimate, even considering the variability of the data and the physiological structure of the pharmacokinetic model. The application in the case study obtained a physiologically reasonable posterior distribution of AUC, with a posterior median close to the value estimated by classic Bailer-type methods. This Bayesian NCA approach for sparse data analysis provides statistical inference on the variability of AUC 0 (∞) -based parameters such as partition coefficient and drug targeting index, so that the comparison of these parameters following destructive sampling becomes statistically feasible.

Advances in Bayesian Modeling in Educational Research

Science.gov (United States)

Levy, Roy

2016-01-01

In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…

A Bayesian approach to spectral quantitative photoacoustic tomography

International Nuclear Information System (INIS)

Pulkkinen, A; Kaipio, J P; Tarvainen, T; Cox, B T; Arridge, S R

2014-01-01

A Bayesian approach to the optical reconstruction problem associated with spectral quantitative photoacoustic tomography is presented. The approach is derived for commonly used spectral tissue models of optical absorption and scattering: the absorption is described as a weighted sum of absorption spectra of known chromophores (spatially dependent chromophore concentrations), while the scattering is described using Mie scattering theory, with the proportionality constant and spectral power law parameter both spatially-dependent. It is validated using two-dimensional test problems composed of three biologically relevant chromophores: fat, oxygenated blood and deoxygenated blood. Using this approach it is possible to estimate the Grüneisen parameter, the absolute chromophore concentrations, and the Mie scattering parameters associated with spectral photoacoustic tomography problems. In addition, the direct estimation of the spectral parameters is compared to estimates obtained by fitting the spectral parameters to estimates of absorption, scattering and Grüneisen parameter at the investigated wavelengths. It is shown with numerical examples that the direct estimation results in better accuracy of the estimated parameters. (papers)

bayesQR: A Bayesian Approach to Quantile Regression

Directory of Open Access Journals (Sweden)

Dries F. Benoit

2017-01-01

Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

A strongly quasiconvex PAC-Bayesian bound

DEFF Research Database (Denmark)

Thiemann, Niklas; Igel, Christian; Wintenberger, Olivier

2017-01-01

We propose a new PAC-Bayesian bound and a way of constructing a hypothesis space, so that the bound is convex in the posterior distribution and also convex in a trade-off parameter between empirical performance of the posterior distribution and its complexity. The complexity is measured by the Ku...

A dynamic Bayesian network based approach to safety decision support in tunnel construction

International Nuclear Information System (INIS)

Wu, Xianguo; Liu, Huitao; Zhang, Limao; Skibniewski, Miroslaw J.; Deng, Qianli; Teng, Jiaying

2015-01-01

This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time. The proposed DBN-based approach can accurately illustrate the dynamic and updated feature of geological, design and mechanical variables as the construction progress evolves, in order to overcome deficiencies of traditional fault analysis methods. Adopting the predictive, sensitivity and diagnostic analysis techniques in the DBN inference, this approach is able to perform feed-forward, concurrent and back-forward control respectively on a quantitative basis, and provide real-time support before and after an accident. A case study in relating to dynamic safety analysis in the construction of Wuhan Yangtze Metro Tunnel in China is used to verify the feasibility of the proposed approach, as well as its application potential. The relationships between the DBN-based and BN-based approaches are further discussed according to analysis results. The proposed approach can be used as a decision tool to provide support for safety analysis in tunnel construction, and thus increase the likelihood of a successful project in a dynamic project environment. - Highlights: • A dynamic Bayesian network (DBN) based approach for safety decision support is developed. • This approach is able to perform feed-forward, concurrent and back-forward analysis and control. • A case concerning dynamic safety analysis in Wuhan Yangtze Metro Tunnel in China is presented. • DBN-based approach can perform a higher accuracy than traditional static BN-based approach

Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it

NARCIS (Netherlands)

P.D. Grünwald (Peter); T. van Ommen (Thijs)

2017-01-01

textabstractWe empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting. We use the standard linear model, which assumes homoskedasticity, whereas the data

Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It

NARCIS (Netherlands)

Grünwald, P.; van Ommen, T.

2017-01-01

We empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting. We use the standard linear model, which assumes homoskedasticity, whereas the data are

How rational should bioethics be? The value of empirical approaches.

Science.gov (United States)

Alvarez, A A

2001-10-01

Rational justification of claims with empirical content calls for empirical and not only normative philosophical investigation. Empirical approaches to bioethics are epistemically valuable, i.e., such methods may be necessary in providing and verifying basic knowledge about cultural values and norms. Our assumptions in moral reasoning can be verified or corrected using these methods. Moral arguments can be initiated or adjudicated by data drawn from empirical investigation. One may argue that individualistic informed consent, for example, is not compatible with the Asian communitarian orientation. But this normative claim uses an empirical assumption that may be contrary to the fact that some Asians do value and argue for informed consent. Is it necessary and factual to neatly characterize some cultures as individualistic and some as communitarian? Empirical investigation can provide a reasonable way to inform such generalizations. In a multi-cultural context, such as in the Philippines, there is a need to investigate the nature of the local ethos before making any appeal to authenticity. Otherwise we may succumb to the same ethical imperialism we are trying hard to resist. Normative claims that involve empirical premises cannot be reasonable verified or evaluated without utilizing empirical methods along with philosophical reflection. The integration of empirical methods to the standard normative approach to moral reasoning should be reasonably guided by the epistemic demands of claims arising from cross-cultural discourse in bioethics.

A bayesian approach to classification criteria for spectacled eiders

Science.gov (United States)

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

Science.gov (United States)

Wong, Rowena Syn Yin; Ismail, Noor Azina

2016-01-01

There are not many studies that attempt to model intensive care unit (ICU) risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU. This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV) model. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method. The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS) was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC) values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05) for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study. Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of in-ICU mortality outcomes.

Interactive Instruction in Bayesian Inference

DEFF Research Database (Denmark)

Khan, Azam; Breslav, Simon; Hornbæk, Kasper

2018-01-01

An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....

A comparison of the Bayesian and frequentist approaches to estimation

CERN Document Server

Samaniego, Francisco J

2010-01-01

This monograph contributes to the area of comparative statistical inference. Attention is restricted to the important subfield of statistical estimation. The book is intended for an audience having a solid grounding in probability and statistics at the level of the year-long undergraduate course taken by statistics and mathematics majors. The necessary background on Decision Theory and the frequentist and Bayesian approaches to estimation is presented and carefully discussed in Chapters 1-3. The 'threshold problem' - identifying the boundary between Bayes estimators which tend to outperform st

Bayesian Network Induction via Local Neighborhoods

National Research Council Canada - National Science Library

Margaritis, Dimitris

1999-01-01

.... We present an efficient algorithm for learning Bayesian networks from data. Our approach constructs Bayesian networks by first identifying each node's Markov blankets, then connecting nodes in a consistent way...

A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features

Directory of Open Access Journals (Sweden)

Dong Hua

2009-01-01

Full Text Available Accurate assessment of skeletal maturity is important clinically. Skeletal age assessment is usually based on features encoded in ossification centers. Therefore, it is critical to design a mechanism to capture as much as possible characteristics of features. We have observed that given a feature, there exist stages of the skeletal age such that the variation pattern of the feature differs in these stages. Based on this observation, we propose a Bayesian cut fitting to describe features in response to the skeletal age. With our approach, appropriate positions for stage separation are determined automatically by a Bayesian approach, and a model is used to fit the variation of a feature within each stage. Our experimental results show that the proposed method surpasses the traditional fitting using only one line or one curve not only in the efficiency and accuracy of fitting but also in global and local feature characterization.

Calibration of environmental radionuclide transfer models using a Bayesian approach with Markov chain Monte Carlo simulations and model comparisons - Calibration of radionuclides transfer models in the environment using a Bayesian approach with Markov chain Monte Carlo simulation and comparison of models

Energy Technology Data Exchange (ETDEWEB)

Nicoulaud-Gouin, V.; Giacalone, M.; Gonze, M.A. [Institut de Radioprotection et de Surete Nucleaire-PRP-ENV/SERIS/LM2E (France); Martin-Garin, A.; Garcia-Sanchez, L. [IRSN-PRP-ENV/SERIS/L2BT (France)

2014-07-01

Calibration of transfer models according to observation data is a challenge, especially if parameters uncertainty is required, and if competing models should be decided between them. Generally two main calibration methods are used: The frequentist approach in which the unknown parameter of interest is supposed fixed and its estimation is based on the data only. In this category, least squared method has many restrictions in nonlinear models and competing models need to be nested in order to be compared. The bayesian inference in which the unknown parameter of interest is supposed random and its estimation is based on the data and on prior information. Compared to frequentist method, it provides probability density functions and therefore pointwise estimation with credible intervals. However, in practical cases, Bayesian inference is a complex problem of numerical integration, which explains its low use in operational modeling including radioecology. This study aims to illustrate the interest and feasibility of Bayesian approach in radioecology particularly in the case of ordinary differential equations with non-constant coefficients models, which cover most radiological risk assessment models, notably those implemented in the Symbiose platform (Gonze et al, 2010). Markov Chain Monte Carlo (MCMC) method (Metropolis et al., 1953) was used because the posterior expectations are intractable integrals. The invariant distribution of the parameters was performed by the metropolis-Hasting algorithm (Hastings, 1970). GNU-MCSim software (Bois and Maszle, 2011) a bayesian hierarchical framework, was used to deal with nonlinear differential models. Two case studies including this type of model were investigated: An Equilibrium Kinetic sorption model (EK) (e.g. van Genuchten et al, 1974), with experimental data concerning {sup 137}Cs and {sup 85}Sr sorption and desorption in different soils studied in stirred flow-through reactors. This model, generalizing the K{sub d} approach

Assessing high reliability via Bayesian approach and accelerated tests

International Nuclear Information System (INIS)

Erto, Pasquale; Giorgio, Massimiliano

2002-01-01

Sometimes the assessment of very high reliability levels is difficult for the following main reasons: - the high reliability level of each item makes it impossible to obtain, in a reasonably short time, a sufficient number of failures; - the high cost of the high reliability items to submit to life tests makes it unfeasible to collect enough data for 'classical' statistical analyses. In the above context, this paper presents a Bayesian solution to the problem of estimation of the parameters of the Weibull-inverse power law model, on the basis of a limited number (say six) of life tests, carried out at different stress levels, all higher than the normal one. The over-stressed (i.e. accelerated) tests allow the use of experimental data obtained in a reasonably short time. The Bayesian approach enables one to reduce the required number of failures adding to the failure information the available a priori engineers' knowledge. This engineers' involvement conforms to the most advanced management policy that aims at involving everyone's commitment in order to obtain total quality. A Monte Carlo study of the non-asymptotic properties of the proposed estimators and a comparison with the properties of maximum likelihood estimators closes the work

Bayesian approach in MN low dose of radiation counting

International Nuclear Information System (INIS)

Serna Berna, A.; Alcaraz, M.; Acevedo, C.; Navarro, J. L.; Alcanzar, M. D.; Canteras, M.

2006-01-01

The Micronucleus assay in lymphocytes is a well established technique for the assessment of genetic damage induced by ionizing radiation. Due to the presence of a natural background of MN the net MN is obtained by subtracting this value to the gross value. When very low doses of radiation are given the induced MN is close even lower than the predetermined background value. Furthermore, the damage distribution induced by the radiation follows a Poisson probability distribution. These two facts pose a difficult task to obtain the net counting rate in the exposed situations. It is possible to overcome this problem using a bayesian approach, in which the selection of a priori distributions for the background and net counting rate plays an important role. In the present work we make a detailed analysed using bayesian theory to infer the net counting rate in two different situations: a) when the background is known for an individual sample, using exact value value for the background and Jeffreys prior for the net counting rate, and b) when the background is not known and we make use of a population background distribution as background prior function and constant prior for the net counting rate. (Author)

Bayesian approach for peak detection in two-dimensional chromatography.

Science.gov (United States)

Vivó-Truyols, Gabriel

2012-03-20

A new method for peak detection in two-dimensional chromatography is presented. In a first step, the method starts with a conventional one-dimensional peak detection algorithm to detect modulated peaks. In a second step, a sophisticated algorithm is constructed to decide which of the individual one-dimensional peaks have been originated from the same compound and should then be arranged in a two-dimensional peak. The merging algorithm is based on Bayesian inference. The user sets prior information about certain parameters (e.g., second-dimension retention time variability, first-dimension band broadening, chromatographic noise). On the basis of these priors, the algorithm calculates the probability of myriads of peak arrangements (i.e., ways of merging one-dimensional peaks), finding which of them holds the highest value. Uncertainty in each parameter can be accounted by adapting conveniently its probability distribution function, which in turn may change the final decision of the most probable peak arrangement. It has been demonstrated that the Bayesian approach presented in this paper follows the chromatographers' intuition. The algorithm has been applied and tested with LC × LC and GC × GC data and takes around 1 min to process chromatograms with several thousands of peaks.

Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach

Science.gov (United States)

Kashuba, Roxolana; McMahon, Gerard; Cuffney, Thomas F.; Qian, Song; Reckhow, Kenneth; Gerritsen, Jeroen; Davies, Susan

2012-01-01

Urban development alters important physical, chemical, and biological processes that define urban stream ecosystems. An approach was developed for quantifying the effects of these processes on aquatic biota, and then linking those effects to endpoints that can be used for environmental management. These complex, interacting systems are challenging to model from a scientific standpoint. A desirable model clearly shows the system, simulates the interactions, and ultimately predicts results of management actions. Traditional regression techniques that calculate empirical relations between pairs of environmental factors do not capture the interconnected web of multiple stressors, but urban development effects are not yet understood at the detailed scales required to make mechanistic modeling approaches feasible. Therefore, in contrast to a fully deterministic or fully statistical modeling approach, a Bayesian network model provides a hybrid approach that can be used to represent known general associations between variables while acknowledging uncertainty in predicted outcomes. It does so by quantifying an expert-elicited network of probabilistic relations between variables. Advantages of this modeling approach include (1) flexibility in accommodating many model specifications and information types; (2) efficiency in storing and manipulating complex information, and to parameterize; and (3) transparency in describing the relations using nodes and arrows and in describing uncertainties with discrete probability distributions for each variable.

Bayesian Approaches to Imputation, Hypothesis Testing, and Parameter Estimation

Science.gov (United States)

Ross, Steven J.; Mackey, Beth

2015-01-01

This chapter introduces three applications of Bayesian inference to common and novel issues in second language research. After a review of the critiques of conventional hypothesis testing, our focus centers on ways Bayesian inference can be used for dealing with missing data, for testing theory-driven substantive hypotheses without a default null…

Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach.

Science.gov (United States)

Fu, Zhibiao; Baker, Daniel; Cheng, Aili; Leighton, Julie; Appelbaum, Edward; Aon, Juan

2016-05-01

The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016. © 2016 American Institute of Chemical Engineers.

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.

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)

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

Science.gov (United States)

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function

An evaluation of the Bayesian approach to fitting the N-mixture model for use with pseudo-replicated count data

Science.gov (United States)

Toribo, S.G.; Gray, B.R.; Liang, S.

2011-01-01

The N-mixture model proposed by Royle in 2004 may be used to approximate the abundance and detection probability of animal species in a given region. In 2006, Royle and Dorazio discussed the advantages of using a Bayesian approach in modelling animal abundance and occurrence using a hierarchical N-mixture model. N-mixture models assume replication on sampling sites, an assumption that may be violated when the site is not closed to changes in abundance during the survey period or when nominal replicates are defined spatially. In this paper, we studied the robustness of a Bayesian approach to fitting the N-mixture model for pseudo-replicated count data. Our simulation results showed that the Bayesian estimates for abundance and detection probability are slightly biased when the actual detection probability is small and are sensitive to the presence of extra variability within local sites.

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.

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.

Bayesian Statistics: Concepts and Applications in Animal Breeding – A Review

Directory of Open Access Journals (Sweden)

Lsxmikant-Sambhaji Kokate

2011-07-01

Full Text Available Statistics uses two major approaches- conventional (or frequentist and Bayesian approach. Bayesian approach provides a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods solve many of the difficulties faced by conventional statistical methods, and extend the applicability of statistical methods. It exploits the use of probabilistic models to formulate scientific problems. To use Bayesian statistics, there is computational difficulty and secondly, Bayesian methods require specifying prior probability distributions. Markov Chain Monte-Carlo (MCMC methods were applied to overcome the computational difficulty, and interest in Bayesian methods was renewed. In Bayesian statistics, Bayesian structural equation model (SEM is used. It provides a powerful and flexible approach for studying quantitative traits for wide spectrum problems and thus it has no operational difficulties, with the exception of some complex cases. In this method, the problems are solved at ease, and the statisticians feel it comfortable with the particular way of expressing the results and employing the software available to analyze a large variety of problems.

Bayesian and neural networks for preliminary ship design

DEFF Research Database (Denmark)

Clausen, H. B.; Lützen, Marie; Friis-Hansen, Andreas

2001-01-01

000 ships is acquired and various methods for derivation of empirical relations are employed. A regression analysis is carried out to fit functions to the data. Further, the data are used to learn Bayesian and neural networks to encode the relations between the characteristics. On the basis...

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

DEFF Research Database (Denmark)

Brouwer, Thomas; Frellsen, Jes; Liò, Pietro

2017-01-01

In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri......-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real...

A Bayesian Approach to Interactive Retrieval

Science.gov (United States)

Tague, Jean M.

1973-01-01

A probabilistic model for interactive retrieval is presented. Bayesian statistical decision theory principles are applied: use of prior and sample information about the relationship of document descriptions to query relevance; maximization of expected value of a utility function, to the problem of optimally restructuring search strategies in an…

Banking Crisis Early Warning Model based on a Bayesian Model Averaging Approach

Directory of Open Access Journals (Sweden)

Taha Zaghdoudi

2016-08-01

Full Text Available The succession of banking crises in which most have resulted in huge economic and financial losses, prompted several authors to study their determinants. These authors constructed early warning models to prevent their occurring. It is in this same vein as our study takes its inspiration. In particular, we have developed a warning model of banking crises based on a Bayesian approach. The results of this approach have allowed us to identify the involvement of the decline in bank profitability, deterioration of the competitiveness of the traditional intermediation, banking concentration and higher real interest rates in triggering bank crisis.

Predicting Graduation Rates at 4-Year Broad Access Institutions Using a Bayesian Modeling Approach

Science.gov (United States)

Crisp, Gloria; Doran, Erin; Salis Reyes, Nicole A.

2018-01-01

This study models graduation rates at 4-year broad access institutions (BAIs). We examine the student body, structural-demographic, and financial characteristics that best predict 6-year graduation rates across two time periods (2008-2009 and 2014-2015). A Bayesian model averaging approach is utilized to account for uncertainty in variable…

An Application of Bayesian Approach in Modeling Risk of Death in an Intensive Care Unit.

Directory of Open Access Journals (Sweden)

Rowena Syn Yin Wong

Full Text Available There are not many studies that attempt to model intensive care unit (ICU risk of death in developing countries, especially in South East Asia. The aim of this study was to propose and describe application of a Bayesian approach in modeling in-ICU deaths in a Malaysian ICU.This was a prospective study in a mixed medical-surgery ICU in a multidisciplinary tertiary referral hospital in Malaysia. Data collection included variables that were defined in Acute Physiology and Chronic Health Evaluation IV (APACHE IV model. Bayesian Markov Chain Monte Carlo (MCMC simulation approach was applied in the development of four multivariate logistic regression predictive models for the ICU, where the main outcome measure was in-ICU mortality risk. The performance of the models were assessed through overall model fit, discrimination and calibration measures. Results from the Bayesian models were also compared against results obtained using frequentist maximum likelihood method.The study involved 1,286 consecutive ICU admissions between January 1, 2009 and June 30, 2010, of which 1,111 met the inclusion criteria. Patients who were admitted to the ICU were generally younger, predominantly male, with low co-morbidity load and mostly under mechanical ventilation. The overall in-ICU mortality rate was 18.5% and the overall mean Acute Physiology Score (APS was 68.5. All four models exhibited good discrimination, with area under receiver operating characteristic curve (AUC values approximately 0.8. Calibration was acceptable (Hosmer-Lemeshow p-values > 0.05 for all models, except for model M3. Model M1 was identified as the model with the best overall performance in this study.Four prediction models were proposed, where the best model was chosen based on its overall performance in this study. This study has also demonstrated the promising potential of the Bayesian MCMC approach as an alternative in the analysis and modeling of in-ICU mortality outcomes.

Statistical detection of EEG synchrony using empirical bayesian inference.

Directory of Open Access Journals (Sweden)

Archana K Singh

Full Text Available There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001 for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

Statistical detection of EEG synchrony using empirical bayesian inference.

Science.gov (United States)

Singh, Archana K; Asoh, Hideki; Takeda, Yuji; Phillips, Steven

2015-01-01

There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

Science.gov (United States)

Cai, Baoping; Xie, Min; Liu, Yonghong; Liu, Yiliu; Ji, Renjie; Feng, Qiang

2017-10-01

The word resilience originally originates from the Latin word "resiliere", which means to "bounce back". The concept has been used in various fields, such as ecology, economics, psychology, and society, with different definitions. In the field of critical infrastructure, although some resilience metrics are proposed, they are totally different from each other, which are determined by the performances of the objects of evaluation. Here we bridge the gap by developing a universal critical infrastructure resilience metric from the perspective of reliability engineering. A dynamic Bayesian networks-based assessment approach is proposed to calculate the resilience value. A series, parallel and voting system is used to demonstrate the application of the developed resilience metric and assessment approach.

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.

Empirical intrinsic geometry for nonlinear modeling and time series filtering.

Science.gov (United States)

Talmon, Ronen; Coifman, Ronald R

2013-07-30

In this paper, we present a method for time series analysis based on empirical intrinsic geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information geometry, this method extends existing geometric analysis tools to support stochastic settings and parametrizes the geometry of empirical distributions. However, the statistical models are not required as priors; hence, EIG may be applied to a wide range of real signals without existing definitive models. We show that the inferred model is noise-resilient and invariant under different observation and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements in a sequential manner. These two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and intrinsic geometry into a nonlinear filtering framework. We show applications to nonlinear and non-Gaussian tracking problems as well as to acoustic signal localization.

Bayesian approach to peak deconvolution and library search for high resolution gas chromatography - Mass spectrometry

NARCIS (Netherlands)

Barcaru, A.; Mol, H.G.J.; Tienstra, M.; Vivó-Truyols, G.

2017-01-01

A novel probabilistic Bayesian strategy is proposed to resolve highly coeluting peaks in high-resolution GC-MS (Orbitrap) data. Opposed to a deterministic approach, we propose to solve the problem probabilistically, using a complete pipeline. First, the retention time(s) for a (probabilistic) number

Variations on Bayesian Prediction and Inference

Science.gov (United States)

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Kernel Bayesian ART and ARTMAP.

Science.gov (United States)

Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan

2018-02-01

Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Good fences make for good neighbors but bad science: a review of what improves Bayesian reasoning and why.

Science.gov (United States)

Brase, Gary L; Hill, W Trey

2015-01-01

Bayesian reasoning, defined here as the updating of a posterior probability following new information, has historically been problematic for humans. Classic psychology experiments have tested human Bayesian reasoning through the use of word problems and have evaluated each participant's performance against the normatively correct answer provided by Bayes' theorem. The standard finding is of generally poor performance. Over the past two decades, though, progress has been made on how to improve Bayesian reasoning. Most notably, research has demonstrated that the use of frequencies in a natural sampling framework-as opposed to single-event probabilities-can improve participants' Bayesian estimates. Furthermore, pictorial aids and certain individual difference factors also can play significant roles in Bayesian reasoning success. The mechanics of how to build tasks which show these improvements is not under much debate. The explanations for why naturally sampled frequencies and pictures help Bayesian reasoning remain hotly contested, however, with many researchers falling into ingrained "camps" organized around two dominant theoretical perspectives. The present paper evaluates the merits of these theoretical perspectives, including the weight of empirical evidence, theoretical coherence, and predictive power. By these criteria, the ecological rationality approach is clearly better than the heuristics and biases view. Progress in the study of Bayesian reasoning will depend on continued research that honestly, vigorously, and consistently engages across these different theoretical accounts rather than staying "siloed" within one particular perspective. The process of science requires an understanding of competing points of view, with the ultimate goal being integration.

Bayesian non- and semi-parametric methods and applications

CERN Document Server

Rossi, Peter

2014-01-01

This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number

Uncertainty estimation of a complex water quality model: The influence of Box-Cox transformation on Bayesian approaches and comparison with a non-Bayesian method

Science.gov (United States)

Freni, Gabriele; Mannina, Giorgio

In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised Likelihood Uncertainty Estimation (GLUE). One crucial point in the application of Bayesian method is the formulation of a likelihood function that is conditioned by the hypotheses made regarding model residuals. Statistical transformations, such as the use of Box-Cox equation, are generally used to ensure the homoscedasticity of residuals. However, this practice may affect the reliability of the analysis leading to a wrong uncertainty estimation. The present paper aims to explore the influence of the Box-Cox equation for environmental water quality models. To this end, five cases were considered one of which was the “real” residuals distributions (i.e. drawn from available data). The analysis was applied to the Nocella experimental catchment (Italy) which is an agricultural and semi-urbanised basin where two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. The results show that the uncertainty estimation is greatly affected by residual transformation and a wrong assumption may also affect the evaluation of model uncertainty. The use of less formal methods always provide an overestimation of modelling uncertainty with respect to Bayesian method but such effect is reduced if a wrong assumption is made regarding the

Understanding uncertainty in temperature effects on vector-borne disease: a Bayesian approach

Science.gov (United States)

Johnson, Leah R.; Ben-Horin, Tal; Lafferty, Kevin D.; McNally, Amy; Mordecai, Erin A.; Paaijmans, Krijn P.; Pawar, Samraat; Ryan, Sadie J.

2015-01-01

Extrinsic environmental factors influence the distribution and population dynamics of many organisms, including insects that are of concern for human health and agriculture. This is particularly true for vector-borne infectious diseases like malaria, which is a major source of morbidity and mortality in humans. Understanding the mechanistic links between environment and population processes for these diseases is key to predicting the consequences of climate change on transmission and for developing effective interventions. An important measure of the intensity of disease transmission is the reproductive number R0. However, understanding the mechanisms linking R0 and temperature, an environmental factor driving disease risk, can be challenging because the data available for parameterization are often poor. To address this, we show how a Bayesian approach can help identify critical uncertainties in components of R0 and how this uncertainty is propagated into the estimate of R0. Most notably, we find that different parameters dominate the uncertainty at different temperature regimes: bite rate from 15°C to 25°C; fecundity across all temperatures, but especially ~25–32°C; mortality from 20°C to 30°C; parasite development rate at ~15–16°C and again at ~33–35°C. Focusing empirical studies on these parameters and corresponding temperature ranges would be the most efficient way to improve estimates of R0. While we focus on malaria, our methods apply to improving process-based models more generally, including epidemiological, physiological niche, and species distribution models.

Evolution of Subjective Hurricane Risk Perceptions: A Bayesian Approach

OpenAIRE

David Kelly; David Letson; Forest Nelson; David S. Nolan; Daniel Solis

2009-01-01

This paper studies how individuals update subjective risk perceptions in response to hurricane track forecast information, using a unique data set from an event market, the Hurricane Futures Market (HFM). We derive a theoretical Bayesian framework which predicts how traders update their perceptions of the probability of a hurricane making landfall in a certain range of coastline. Our results suggest that traders behave in a way consistent with Bayesian updating but this behavior is based on t...

Inverse Problems in a Bayesian Setting

KAUST Repository

Matthies, Hermann G.

2016-02-13

In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.

Inverse Problems in a Bayesian Setting

KAUST Repository

Matthies, Hermann G.; Zander, Elmar; Rosić, Bojana V.; Litvinenko, Alexander; Pajonk, Oliver

2016-01-01

In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.

Bayesian inference using WBDev: a tutorial for social scientists

NARCIS (Netherlands)

Wetzels, R.; Lee, M.D.; Wagenmakers, E.-J.

2010-01-01

Over the last decade, the popularity of Bayesian data analysis in the empirical sciences has greatly increased. This is partly due to the availability of WinBUGS, a free and flexible statistical software package that comes with an array of predefined functions and distributions, allowing users to

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.

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

Directory of Open Access Journals (Sweden)

Arunabha Majumdar

2018-02-01

Full Text Available 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.

Reliability assessment using degradation models: bayesian and classical approaches

Directory of Open Access Journals (Sweden)

Marta Afonso Freitas

2010-04-01

Full Text Available Traditionally, reliability assessment of devices has been based on (accelerated life tests. However, for highly reliable products, little information about reliability is provided by life tests in which few or no failures are typically observed. Since most failures arise from a degradation mechanism at work for which there are characteristics that degrade over time, one alternative is monitor the device for a period of time and assess its reliability from the changes in performance (degradation observed during that period. The goal of this article is to illustrate how degradation data can be modeled and analyzed by using "classical" and Bayesian approaches. Four methods of data analysis based on classical inference are presented. Next we show how Bayesian methods can also be used to provide a natural approach to analyzing degradation data. The approaches are applied to a real data set regarding train wheels degradation.Tradicionalmente, o acesso à confiabilidade de dispositivos tem sido baseado em testes de vida (acelerados. Entretanto, para produtos altamente confiáveis, pouca informação a respeito de sua confiabilidade é fornecida por testes de vida no quais poucas ou nenhumas falhas são observadas. Uma vez que boa parte das falhas é induzida por mecanismos de degradação, uma alternativa é monitorar o dispositivo por um período de tempo e acessar sua confiabilidade através das mudanças em desempenho (degradação observadas durante aquele período. O objetivo deste artigo é ilustrar como dados de degradação podem ser modelados e analisados utilizando-se abordagens "clássicas" e Bayesiana. Quatro métodos de análise de dados baseados em inferência clássica são apresentados. A seguir, mostramos como os métodos Bayesianos podem também ser aplicados para proporcionar uma abordagem natural à análise de dados de degradação. As abordagens são aplicadas a um banco de dados real relacionado à degradação de rodas de trens.

Bayesian approach to errors-in-variables in regression models

Science.gov (United States)

Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad

2017-05-01

In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.

Bayesian Reliability Estimation for Deteriorating Systems with Limited Samples Using the Maximum Entropy Approach

Directory of Open Access Journals (Sweden)

Ning-Cong Xiao

2013-12-01

Full Text Available In this paper the combinations of maximum entropy method and Bayesian inference for reliability assessment of deteriorating system is proposed. Due to various uncertainties, less data and incomplete information, system parameters usually cannot be determined precisely. These uncertainty parameters can be modeled by fuzzy sets theory and the Bayesian inference which have been proved to be useful for deteriorating systems under small sample sizes. The maximum entropy approach can be used to calculate the maximum entropy density function of uncertainty parameters more accurately for it does not need any additional information and assumptions. Finally, two optimization models are presented which can be used to determine the lower and upper bounds of systems probability of failure under vague environment conditions. Two numerical examples are investigated to demonstrate the proposed method.

Incorporating historical information in biosimilar trials: Challenges and a hybrid Bayesian-frequentist approach.

Science.gov (United States)

Mielke, Johanna; Schmidli, Heinz; Jones, Byron

2018-05-01

For the approval of biosimilars, it is, in most cases, necessary to conduct large Phase III clinical trials in patients to convince the regulatory authorities that the product is comparable in terms of efficacy and safety to the originator product. As the originator product has already been studied in several trials beforehand, it seems natural to include this historical information into the showing of equivalent efficacy. Since all studies for the regulatory approval of biosimilars are confirmatory studies, it is required that the statistical approach has reasonable frequentist properties, most importantly, that the Type I error rate is controlled-at least in all scenarios that are realistic in practice. However, it is well known that the incorporation of historical information can lead to an inflation of the Type I error rate in the case of a conflict between the distribution of the historical data and the distribution of the trial data. We illustrate this issue and confirm, using the Bayesian robustified meta-analytic-predictive (MAP) approach as an example, that simultaneously controlling the Type I error rate over the complete parameter space and gaining power in comparison to a standard frequentist approach that only considers the data in the new study, is not possible. We propose a hybrid Bayesian-frequentist approach for binary endpoints that controls the Type I error rate in the neighborhood of the center of the prior distribution, while improving the power. We study the properties of this approach in an extensive simulation study and provide a real-world example. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Basics of Bayesian reliability estimation from attribute test data

International Nuclear Information System (INIS)

Martz, H.F. Jr.; Waller, R.A.

1975-10-01

The basic notions of Bayesian reliability estimation from attribute lifetest data are presented in an introductory and expository manner. Both Bayesian point and interval estimates of the probability of surviving the lifetest, the reliability, are discussed. The necessary formulas are simply stated, and examples are given to illustrate their use. In particular, a binomial model in conjunction with a beta prior model is considered. Particular attention is given to the procedure for selecting an appropriate prior model in practice. Empirical Bayes point and interval estimates of reliability are discussed and examples are given. 7 figures, 2 tables

Discriminative Bayesian Dictionary Learning for Classification.

Science.gov (United States)

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

A comparison of machine learning and Bayesian modelling for molecular serotyping.

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Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological

Refining mass formulas for astrophysical applications: A Bayesian neural network approach

Science.gov (United States)

Utama, R.; Piekarewicz, J.

2017-10-01

Background: Exotic nuclei, particularly those near the drip lines, are at the core of one of the fundamental questions driving nuclear structure and astrophysics today: What are the limits of nuclear binding? Exotic nuclei play a critical role in both informing theoretical models as well as in our understanding of the origin of the heavy elements. Purpose: Our aim is to refine existing mass models through the training of an artificial neural network that will mitigate the large model discrepancies far away from stability. Methods: The basic paradigm of our two-pronged approach is an existing mass model that captures as much as possible of the underlying physics followed by the implementation of a Bayesian neural network (BNN) refinement to account for the missing physics. Bayesian inference is employed to determine the parameters of the neural network so that model predictions may be accompanied by theoretical uncertainties. Results: Despite the undeniable quality of the mass models adopted in this work, we observe a significant improvement (of about 40%) after the BNN refinement is implemented. Indeed, in the specific case of the Duflo-Zuker mass formula, we find that the rms deviation relative to experiment is reduced from σrms=0.503 MeV to σrms=0.286 MeV. These newly refined mass tables are used to map the neutron drip lines (or rather "drip bands") and to study a few critical r -process nuclei. Conclusions: The BNN approach is highly successful in refining the predictions of existing mass models. In particular, the large discrepancy displayed by the original "bare" models in regions where experimental data are unavailable is considerably quenched after the BNN refinement. This lends credence to our approach and has motivated us to publish refined mass tables that we trust will be helpful for future astrophysical applications.

An approach based on Hierarchical Bayesian Graphical Models for measurement interpretation under uncertainty

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Skataric, Maja; Bose, Sandip; Zeroug, Smaine; Tilke, Peter

2017-02-01

It is not uncommon in the field of non-destructive evaluation that multiple measurements encompassing a variety of modalities are available for analysis and interpretation for determining the underlying states of nature of the materials or parts being tested. Despite and sometimes due to the richness of data, significant challenges arise in the interpretation manifested as ambiguities and inconsistencies due to various uncertain factors in the physical properties (inputs), environment, measurement device properties, human errors, and the measurement data (outputs). Most of these uncertainties cannot be described by any rigorous mathematical means, and modeling of all possibilities is usually infeasible for many real time applications. In this work, we will discuss an approach based on Hierarchical Bayesian Graphical Models (HBGM) for the improved interpretation of complex (multi-dimensional) problems with parametric uncertainties that lack usable physical models. In this setting, the input space of the physical properties is specified through prior distributions based on domain knowledge and expertise, which are represented as Gaussian mixtures to model the various possible scenarios of interest for non-destructive testing applications. Forward models are then used offline to generate the expected distribution of the proposed measurements which are used to train a hierarchical Bayesian network. In Bayesian analysis, all model parameters are treated as random variables, and inference of the parameters is made on the basis of posterior distribution given the observed data. Learned parameters of the posterior distribution obtained after the training can therefore be used to build an efficient classifier for differentiating new observed data in real time on the basis of pre-trained models. We will illustrate the implementation of the HBGM approach to ultrasonic measurements used for cement evaluation of cased wells in the oil industry.

Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage.

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Mejia, Amanda F; Nebel, Mary Beth; Barber, Anita D; Choe, Ann S; Pekar, James J; Caffo, Brian S; Lindquist, Martin A

2018-05-15

Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICC MSE ) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully

Quantum-Like Bayesian Networks for Modeling Decision Making

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Catarina eMoreira

2016-01-01

Full Text Available In this work, we explore an alternative quantum structure to perform quantum probabilistic inferences to accommodate the paradoxical findings of the Sure Thing Principle. We propose a Quantum-Like Bayesian Network, which consists in replacing classical probabilities by quantum probability amplitudes. However, since this approach suffers from the problem of exponential growth of quantum parameters, we also propose a similarity heuristic that automatically fits quantum parameters through vector similarities. This makes the proposed model general and predictive in contrast to the current state of the art models, which cannot be generalized for more complex decision scenarios and that only provide an explanatory nature for the observed paradoxes. In the end, the model that we propose consists in a nonparametric method for estimating inference effects from a statistical point of view. It is a statistical model that is simpler than the previous quantum dynamic and quantum-like models proposed in the literature. We tested the proposed network with several empirical data from the literature, mainly from the Prisoner's Dilemma game and the Two Stage Gambling game. The results obtained show that the proposed quantum Bayesian Network is a general method that can accommodate violations of the laws of classical probability theory and make accurate predictions regarding human decision-making in these scenarios.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

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Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

Integrated survival analysis using an event-time approach in a Bayesian framework.

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Walsh, Daniel P; Dreitz, Victoria J; Heisey, Dennis M

2015-02-01

Event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤ 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.

Integrated survival analysis using an event-time approach in a Bayesian framework

Science.gov (United States)

Walsh, Daniel P.; Dreitz, VJ; Heisey, Dennis M.

2015-01-01

Event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤ 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.

Bayesian-based localization in inhomogeneous transmission media

DEFF Research Database (Denmark)

Nadimi, E. S.; Blanes-Vidal, V.; Johansen, P. M.

2013-01-01

In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network with heteroge......In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network...... with heterogeneous transmission medium as a function of the given RSS measurements and the characteristics of the heterogeneous medium. The results of this study show that the localization mean square error (MSE) of the Bayesian-based method outperformed all other existing localization approaches. © 2013 ACM....

Bayesian Option Pricing Framework with Stochastic Volatility for FX Data

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Ying Wang

2016-12-01

Full Text Available The application of stochastic volatility (SV models in the option pricing literature usually assumes that the market has sufficient option data to calibrate the model’s risk-neutral parameters. When option data are insufficient or unavailable, market practitioners must estimate the model from the historical returns of the underlying asset and then transform the resulting model into its risk-neutral equivalent. However, the likelihood function of an SV model can only be expressed in a high-dimensional integration, which makes the estimation a highly challenging task. The Bayesian approach has been the classical way to estimate SV models under the data-generating (physical probability measure, but the transformation from the estimated physical dynamic into its risk-neutral counterpart has not been addressed. Inspired by the generalized autoregressive conditional heteroskedasticity (GARCH option pricing approach by Duan in 1995, we propose an SV model that enables us to simultaneously and conveniently perform Bayesian inference and transformation into risk-neutral dynamics. Our model relaxes the normality assumption on innovations of both return and volatility processes, and our empirical study shows that the estimated option prices generate realistic implied volatility smile shapes. In addition, the volatility premium is almost flat across strike prices, so adding a few option data to the historical time series of the underlying asset can greatly improve the estimation of option prices.

A Dynamic BI–Orthogonal Field Equation Approach to Efficient Bayesian Inversion

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Tagade Piyush M.

2017-06-01

Full Text Available This paper proposes a novel computationally efficient stochastic spectral projection based approach to Bayesian inversion of a computer simulator with high dimensional parametric and model structure uncertainty. The proposed method is based on the decomposition of the solution into its mean and a random field using a generic Karhunen-Loève expansion. The random field is represented as a convolution of separable Hilbert spaces in stochastic and spatial dimensions that are spectrally represented using respective orthogonal bases. In particular, the present paper investigates generalized polynomial chaos bases for the stochastic dimension and eigenfunction bases for the spatial dimension. Dynamic orthogonality is used to derive closed-form equations for the time evolution of mean, spatial and the stochastic fields. The resultant system of equations consists of a partial differential equation (PDE that defines the dynamic evolution of the mean, a set of PDEs to define the time evolution of eigenfunction bases, while a set of ordinary differential equations (ODEs define dynamics of the stochastic field. This system of dynamic evolution equations efficiently propagates the prior parametric uncertainty to the system response. The resulting bi-orthogonal expansion of the system response is used to reformulate the Bayesian inference for efficient exploration of the posterior distribution. The efficacy of the proposed method is investigated for calibration of a 2D transient diffusion simulator with an uncertain source location and diffusivity. The computational efficiency of the method is demonstrated against a Monte Carlo method and a generalized polynomial chaos approach.

Inference of reactive transport model parameters using a Bayesian multivariate approach

Science.gov (United States)

Carniato, Luca; Schoups, Gerrit; van de Giesen, Nick

2014-08-01

Parameter estimation of subsurface transport models from multispecies data requires the definition of an objective function that includes different types of measurements. Common approaches are weighted least squares (WLS), where weights are specified a priori for each measurement, and weighted least squares with weight estimation (WLS(we)) where weights are estimated from the data together with the parameters. In this study, we formulate the parameter estimation task as a multivariate Bayesian inference problem. The WLS and WLS(we) methods are special cases in this framework, corresponding to specific prior assumptions about the residual covariance matrix. The Bayesian perspective allows for generalizations to cases where residual correlation is important and for efficient inference by analytically integrating out the variances (weights) and selected covariances from the joint posterior. Specifically, the WLS and WLS(we) methods are compared to a multivariate (MV) approach that accounts for specific residual correlations without the need for explicit estimation of the error parameters. When applied to inference of reactive transport model parameters from column-scale data on dissolved species concentrations, the following results were obtained: (1) accounting for residual correlation between species provides more accurate parameter estimation for high residual correlation levels whereas its influence for predictive uncertainty is negligible, (2) integrating out the (co)variances leads to an efficient estimation of the full joint posterior with a reduced computational effort compared to the WLS(we) method, and (3) in the presence of model structural errors, none of the methods is able to identify the correct parameter values.

Prior approval: the growth of Bayesian methods in psychology.

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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.

Identifying Similarities in Cognitive Subtest Functional Requirements: An Empirical Approach

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Frisby, Craig L.; Parkin, Jason R.

2007-01-01

In the cognitive test interpretation literature, a Rational/Intuitive, Indirect Empirical, or Combined approach is typically used to construct conceptual taxonomies of the functional (behavioral) similarities between subtests. To address shortcomings of these approaches, the functional requirements for 49 subtests from six individually…

A Bayesian approach to degradation-based burn-in optimization for display products exhibiting two-phase degradation patterns

International Nuclear Information System (INIS)

Yuan, Tao; Bae, Suk Joo; Zhu, Xiaoyan

2016-01-01

Motivated by the two-phase degradation phenomena observed in light displays (e.g., plasma display panels (PDPs), organic light emitting diodes (OLEDs)), this study proposes a new degradation-based burn-in testing plan for display products exhibiting two-phase degradation patterns. The primary focus of the burn-in test in this study is to eliminate the initial rapid degradation phase, while the major purpose of traditional burn-in tests is to detect and eliminate early failures from weak units. A hierarchical Bayesian bi-exponential model is used to capture two-phase degradation patterns of the burn-in population. Mission reliability and total cost are introduced as planning criteria. The proposed burn-in approach accounts for unit-to-unit variability within the burn-in population, and uncertainty concerning the model parameters, mainly in the hierarchical Bayesian framework. Available pre-burn-in data is conveniently incorporated into the burn-in decision-making procedure. A practical example of PDP degradation data is used to illustrate the proposed methodology. The proposed method is compared to other approaches such as the maximum likelihood method or the change-point regression. - Highlights: • We propose a degradation-based burn-in test for products with two-phase degradation. • Mission reliability and total cost are used as planning criteria. • The proposed burn-in approach is built within the hierarchical Bayesian framework. • A practical example was used to illustrate the proposed methodology.

Bayesian estimation of the discrete coefficient of determination.

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Chen, Ting; Braga-Neto, Ulisses M

2016-12-01

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

Bridging process-based and empirical approaches to modeling tree growth

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Harry T. Valentine; Annikki Makela; Annikki Makela

2005-01-01

The gulf between process-based and empirical approaches to modeling tree growth may be bridged, in part, by the use of a common model. To this end, we have formulated a process-based model of tree growth that can be fitted and applied in an empirical mode. The growth model is grounded in pipe model theory and an optimal control model of crown development. Together, the...

A hierarchical bayesian approach to ecological count data: a flexible tool for ecologists.

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James A Fordyce

Full Text Available Many ecological studies use the analysis of count data to arrive at biologically meaningful inferences. Here, we introduce a hierarchical bayesian approach to count data. This approach has the advantage over traditional approaches in that it directly estimates the parameters of interest at both the individual-level and population-level, appropriately models uncertainty, and allows for comparisons among models, including those that exceed the complexity of many traditional approaches, such as ANOVA or non-parametric analogs. As an example, we apply this method to oviposition preference data for butterflies in the genus Lycaeides. Using this method, we estimate the parameters that describe preference for each population, compare the preference hierarchies among populations, and explore various models that group populations that share the same preference hierarchy.

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

Précis of bayesian rationality: The probabilistic approach to human reasoning.

Science.gov (United States)

Oaksford, Mike; Chater, Nick

2009-02-01

According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic--the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining the end-point of cognitive development; and contemporary psychology of reasoning has focussed on comparing human reasoning against logical standards. Bayesian Rationality argues that rationality is defined instead by the ability to reason about uncertainty. Although people are typically poor at numerical reasoning about probability, human thought is sensitive to subtle patterns of qualitative Bayesian, probabilistic reasoning. In Chapters 1-4 of Bayesian Rationality (Oaksford & Chater 2007), the case is made that cognition in general, and human everyday reasoning in particular, is best viewed as solving probabilistic, rather than logical, inference problems. In Chapters 5-7 the psychology of "deductive" reasoning is tackled head-on: It is argued that purportedly "logical" reasoning problems, revealing apparently irrational behaviour, are better understood from a probabilistic point of view. Data from conditional reasoning, Wason's selection task, and syllogistic inference are captured by recasting these problems probabilistically. The probabilistic approach makes a variety of novel predictions which have been experimentally confirmed. The book considers the implications of this work, and the wider "probabilistic turn" in cognitive science and artificial intelligence, for understanding human rationality.

Bayesian Recovery of Clipped OFDM Signals: A Receiver-based Approach

KAUST Repository

Al-Rabah, Abdullatif R.

2013-05-01

Recently, orthogonal frequency-division multiplexing (OFDM) has been adopted for high-speed wireless communications due to its robustness against multipath fading. However, one of the main fundamental drawbacks of OFDM systems is the high peak-to-average-power ratio (PAPR). Several techniques have been proposed for PAPR reduction. Most of these techniques require transmitter-based (pre-compensated) processing. On the other hand, receiver-based alternatives would save the power and reduce the transmitter complexity. By keeping this in mind, a possible approach is to limit the amplitude of the OFDM signal to a predetermined threshold and equivalently a sparse clipping signal is added. Then, estimating this clipping signal at the receiver to recover the original signal. In this work, we propose a Bayesian receiver-based low-complexity clipping signal recovery method for PAPR reduction. The method is able to i) effectively reduce the PAPR via simple clipping scheme at the transmitter side, ii) use Bayesian recovery algorithm to reconstruct the clipping signal at the receiver side by measuring part of subcarriers, iii) perform well in the absence of statistical information about the signal (e.g. clipping level) and the noise (e.g. noise variance), and at the same time iv is energy efficient due to its low complexity. Specifically, the proposed recovery technique is implemented in data-aided based. The data-aided method collects clipping information by measuring reliable 
data subcarriers, thus makes full use of spectrum for data transmission without the need for tone reservation. The study is extended further to discuss how to improve the recovery of the clipping signal utilizing some features of practical OFDM systems i.e., the oversampling and the presence of multiple receivers. Simulation results demonstrate the superiority of the proposed technique over other recovery algorithms. The overall objective is to show that the receiver-based Bayesian technique is highly

A study of finite mixture model: Bayesian approach on financial time series data

Science.gov (United States)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-07-01

Recently, statistician have emphasized on the fitting finite mixture model by using Bayesian method. Finite mixture model is a mixture of distributions in modeling a statistical distribution meanwhile Bayesian method is a statistical method that use to fit the mixture model. Bayesian method is being used widely because it has asymptotic properties which provide remarkable result. In addition, Bayesian method also shows consistency characteristic which means the parameter estimates are close to the predictive distributions. In the present paper, the number of components for mixture model is studied by using Bayesian Information Criterion. Identify the number of component is important because it may lead to an invalid result. Later, the Bayesian method is utilized to fit the k-component mixture model in order to explore the relationship between rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia. Lastly, the results showed that there is a negative effect among rubber price and stock market price for all selected countries.

Assessing compositional variability through graphical analysis and Bayesian statistical approaches: case studies on transgenic crops.

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Harrigan, George G; Harrison, Jay M

2012-01-01

New transgenic (GM) crops are subjected to extensive safety assessments that include compositional comparisons with conventional counterparts as a cornerstone of the process. The influence of germplasm, location, environment, and agronomic treatments on compositional variability is, however, often obscured in these pair-wise comparisons. Furthermore, classical statistical significance testing can often provide an incomplete and over-simplified summary of highly responsive variables such as crop composition. In order to more clearly describe the influence of the numerous sources of compositional variation we present an introduction to two alternative but complementary approaches to data analysis and interpretation. These include i) exploratory data analysis (EDA) with its emphasis on visualization and graphics-based approaches and ii) Bayesian statistical methodology that provides easily interpretable and meaningful evaluations of data in terms of probability distributions. The EDA case-studies include analyses of herbicide-tolerant GM soybean and insect-protected GM maize and soybean. Bayesian approaches are presented in an analysis of herbicide-tolerant GM soybean. Advantages of these approaches over classical frequentist significance testing include the more direct interpretation of results in terms of probabilities pertaining to quantities of interest and no confusion over the application of corrections for multiple comparisons. It is concluded that a standardized framework for these methodologies could provide specific advantages through enhanced clarity of presentation and interpretation in comparative assessments of crop composition.

A Bayesian trans-dimensional approach for the fusion of multiple geophysical datasets

Science.gov (United States)

JafarGandomi, Arash; Binley, Andrew

2013-09-01

We propose a Bayesian fusion approach to integrate multiple geophysical datasets with different coverage and sensitivity. The fusion strategy is based on the capability of various geophysical methods to provide enough resolution to identify either subsurface material parameters or subsurface structure, or both. We focus on electrical resistivity as the target material parameter and electrical resistivity tomography (ERT), electromagnetic induction (EMI), and ground penetrating radar (GPR) as the set of geophysical methods. However, extending the approach to different sets of geophysical parameters and methods is straightforward. Different geophysical datasets are entered into a trans-dimensional Markov chain Monte Carlo (McMC) search-based joint inversion algorithm. The trans-dimensional property of the McMC algorithm allows dynamic parameterisation of the model space, which in turn helps to avoid bias of the post-inversion results towards a particular model. Given that we are attempting to develop an approach that has practical potential, we discretize the subsurface into an array of one-dimensional earth-models. Accordingly, the ERT data that are collected by using two-dimensional acquisition geometry are re-casted to a set of equivalent vertical electric soundings. Different data are inverted either individually or jointly to estimate one-dimensional subsurface models at discrete locations. We use Shannon's information measure to quantify the information obtained from the inversion of different combinations of geophysical datasets. Information from multiple methods is brought together via introducing joint likelihood function and/or constraining the prior information. A Bayesian maximum entropy approach is used for spatial fusion of spatially dispersed estimated one-dimensional models and mapping of the target parameter. We illustrate the approach with a synthetic dataset and then apply it to a field dataset. We show that the proposed fusion strategy is

Collective animal behavior from Bayesian estimation and probability matching.

Directory of Open Access Journals (Sweden)

Alfonso Pérez-Escudero

2011-11-01

Full Text Available Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior.

A Bayesian Approach to Excess Volatility, Short-term Underreaction and Long-term Overreaction during Financial Crises

NARCIS (Netherlands)

X. Guo (Xu); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung); L. Zhu (Lixing)

2016-01-01

textabstractIn this paper, we introduce a new Bayesian approach to explain some market anomalies during financial crises and subsequent recovery. We assume that the earnings shock of an asset follows a random walk model with and without drift to incorporate the impact of financial crises. We further

Applying a Bayesian Approach to Identification of Orthotropic Elastic Constants from Full Field Displacement Measurements

Directory of Open Access Journals (Sweden)

Le Riche R.

2010-06-01

Full Text Available A major challenge in the identification of material properties is handling different sources of uncertainty in the experiment and the modelling of the experiment for estimating the resulting uncertainty in the identified properties. Numerous improvements in identification methods have provided increasingly accurate estimates of various material properties. However, characterizing the uncertainty in the identified properties is still relatively crude. Different material properties obtained from a single test are not obtained with the same confidence. Typically the highest uncertainty is associated with respect to properties to which the experiment is the most insensitive. In addition, the uncertainty in different properties can be strongly correlated, so that obtaining only variance estimates may be misleading. A possible approach for handling the different sources of uncertainty and estimating the uncertainty in the identified properties is the Bayesian method. This method was introduced in the late 1970s in the context of identification [1] and has been applied since to different problems, notably identification of elastic constants from plate vibration experiments [2]-[4]. The applications of the method to these classical pointwise tests involved only a small number of measurements (typically ten natural frequencies in the previously cited vibration test which facilitated the application of the Bayesian approach. For identifying elastic constants, full field strain or displacement measurements provide a high number of measured quantities (one measurement per image pixel and hence a promise of smaller uncertainties in the properties. However, the high number of measurements represents also a major computational challenge in applying the Bayesian approach to full field measurements. To address this challenge we propose an approach based on the proper orthogonal decomposition (POD of the full fields in order to drastically reduce their

Applying a Bayesian Approach to Identification of Orthotropic Elastic Constants from Full Field Displacement Measurements

Science.gov (United States)

Gogu, C.; Yin, W.; Haftka, R.; Ifju, P.; Molimard, J.; Le Riche, R.; Vautrin, A.

2010-06-01

A major challenge in the identification of material properties is handling different sources of uncertainty in the experiment and the modelling of the experiment for estimating the resulting uncertainty in the identified properties. Numerous improvements in identification methods have provided increasingly accurate estimates of various material properties. However, characterizing the uncertainty in the identified properties is still relatively crude. Different material properties obtained from a single test are not obtained with the same confidence. Typically the highest uncertainty is associated with respect to properties to which the experiment is the most insensitive. In addition, the uncertainty in different properties can be strongly correlated, so that obtaining only variance estimates may be misleading. A possible approach for handling the different sources of uncertainty and estimating the uncertainty in the identified properties is the Bayesian method. This method was introduced in the late 1970s in the context of identification [1] and has been applied since to different problems, notably identification of elastic constants from plate vibration experiments [2]-[4]. The applications of the method to these classical pointwise tests involved only a small number of measurements (typically ten natural frequencies in the previously cited vibration test) which facilitated the application of the Bayesian approach. For identifying elastic constants, full field strain or displacement measurements provide a high number of measured quantities (one measurement per image pixel) and hence a promise of smaller uncertainties in the properties. However, the high number of measurements represents also a major computational challenge in applying the Bayesian approach to full field measurements. To address this challenge we propose an approach based on the proper orthogonal decomposition (POD) of the full fields in order to drastically reduce their dimensionality. POD is

Quantifying uncertainty and resilience on coral reefs using a Bayesian approach

International Nuclear Information System (INIS)

Van Woesik, R

2013-01-01

Coral reefs are rapidly deteriorating globally. The contemporary management option favors managing for resilience to provide reefs with the capacity to tolerate human-induced disturbances. Yet resilience is most commonly defined as the capacity of a system to absorb disturbances without changing fundamental processes or functionality. Quantifying no change, or the uncertainty of a null hypothesis, is nonsensical using frequentist statistics, but is achievable using a Bayesian approach. This study outlines a practical Bayesian framework that quantifies the resilience of coral reefs using two inter-related models. The first model examines the functionality of coral reefs in the context of their reef-building capacity, whereas the second model examines the recovery rates of coral cover after disturbances. Quantifying intrinsic rates of increase in coral cover and habitat-specific, steady-state equilibria are useful proxies of resilience. A reduction in the intrinsic rate of increase following a disturbance, or the slowing of recovery over time, can be useful indicators of stress; a change in the steady-state equilibrium suggests a phase shift. Quantifying the uncertainty of key reef-building processes and recovery parameters, and comparing these parameters against benchmarks, facilitates the detection of loss of resilience and provides signals of imminent change. (letter)

Quantifying uncertainty and resilience on coral reefs using a Bayesian approach

Science.gov (United States)

van Woesik, R.

2013-12-01

Coral reefs are rapidly deteriorating globally. The contemporary management option favors managing for resilience to provide reefs with the capacity to tolerate human-induced disturbances. Yet resilience is most commonly defined as the capacity of a system to absorb disturbances without changing fundamental processes or functionality. Quantifying no change, or the uncertainty of a null hypothesis, is nonsensical using frequentist statistics, but is achievable using a Bayesian approach. This study outlines a practical Bayesian framework that quantifies the resilience of coral reefs using two inter-related models. The first model examines the functionality of coral reefs in the context of their reef-building capacity, whereas the second model examines the recovery rates of coral cover after disturbances. Quantifying intrinsic rates of increase in coral cover and habitat-specific, steady-state equilibria are useful proxies of resilience. A reduction in the intrinsic rate of increase following a disturbance, or the slowing of recovery over time, can be useful indicators of stress; a change in the steady-state equilibrium suggests a phase shift. Quantifying the uncertainty of key reef-building processes and recovery parameters, and comparing these parameters against benchmarks, facilitates the detection of loss of resilience and provides signals of imminent change.

Bayesian natural language semantics and pragmatics

CERN Document Server

Zeevat, Henk

2015-01-01

The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice's contributions to pragmatics or in interpretation by abduction.

A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups.

Science.gov (United States)

Das, Kiranmoy; Afriyie, Prince; Spirko, Lauren

2015-11-01

Often the biological and/or clinical experiments result in longitudinal data from multiple related groups. The analysis of such data is quite challenging due to the fact that groups might have shared information on the mean and/or covariance functions. In this article, we consider a Bayesian semiparametric approach of modeling the mean trajectories for longitudinal response coming from multiple related groups. We consider matrix stick-breaking process priors on the group mean parameters which allows information sharing on the mean trajectories across the groups. Simulation studies are performed to demonstrate the effectiveness of the proposed approach compared to the more traditional approaches. We analyze data from a one-year follow-up of nutrition education for hypercholesterolemic children with three different treatments where the children are from different age-groups. Our analysis provides more clinically useful information than the previous analysis of the same dataset. The proposed approach will be a very powerful tool for analyzing data from clinical trials and other medical experiments.

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

Directory of Open Access Journals (Sweden)

David W Redding

Full Text Available Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT, to a spatial Bayesian SDM method (fitted using R-INLA, when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

Science.gov (United States)

Redding, David W; Lucas, Tim C D; Blackburn, Tim M; Jones, Kate E

2017-01-01

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial

A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration

Directory of Open Access Journals (Sweden)

Richard N Henson

2011-08-01

Full Text Available We review recent methodological developments within a Parametric Empirical Bayesian (PEB framework for reconstructing intracranial sources of extracranial electroencephalographic (EEG and magnetoencephalographic (MEG data under linear Gaussian assumptions. The PEB framework offers a natural way to integrate multiple constraints (spatial priors on this inverse problem, such as those derived from different modalities (e.g., from functional magnetic resonance imaging, fMRI or from multiple replications (e.g., subjects. Using variations of the same basic generative model, we illustrate the application of PEB to three cases: 1 symmetric integration (fusion of MEG and EEG; 2 asymmetric integration of MEG or EEG with fMRI, and 3 group-optimisation of spatial priors across subjects. We evaluate these applications on multimodal data acquired from 18 subjects, focusing on energy induced by face perception within a time-frequency window of 100-220ms, 8-18Hz. We show the benefits of multi-modal, multi-subject integration in terms of the model evidence and the reproducibility (over subjects of cortical responses to faces.

Prediction of road accidents: A Bayesian hierarchical approach

DEFF Research Database (Denmark)

Deublein, Markus; Schubert, Matthias; Adey, Bryan T.

2013-01-01

the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link......In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson......-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks...

Bayesian estimation of dose rate effectiveness

International Nuclear Information System (INIS)

Arnish, J.J.; Groer, P.G.

2000-01-01

A Bayesian statistical method was used to quantify the effectiveness of high dose rate 137 Cs gamma radiation at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice. The Bayesian approach considers both the temporal and dose dependence of radiation carcinogenesis and total mortality. This paper provides the first direct estimation of dose rate effectiveness using Bayesian statistics. This statistical approach provides a quantitative description of the uncertainty of the factor characterising the dose rate in terms of a probability density function. The results show that a fixed dose from 137 Cs gamma radiation delivered at a high dose rate is more effective at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice than the same dose delivered at a low dose rate. (author)

A Bayesian network approach for modeling local failure in lung cancer

International Nuclear Information System (INIS)

Oh, Jung Hun; Craft, Jeffrey; Al Lozi, Rawan; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O; Bradley, Jeffrey D; El Naqa, Issam

2011-01-01

Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

International Nuclear Information System (INIS)

Xiao, H.; Wu, J.-L.; Wang, J.-X.; Sun, R.; Roy, C.J.

2016-01-01

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has

A Bayesian Belief Network approach to assess the potential of non wood forest products for small scale forest owners

Science.gov (United States)

Vacik, Harald; Huber, Patrick; Hujala, Teppo; Kurtilla, Mikko; Wolfslehner, Bernhard

2015-04-01

It is an integral element of the European understanding of sustainable forest management to foster the design and marketing of forest products, non-wood forest products (NWFPs) and services that go beyond the production of timber. Despite the relevance of NWFPs in Europe, forest management and planning methods have been traditionally tailored towards wood and wood products, because most forest management models and silviculture techniques were developed to ensure a sustained production of timber. Although several approaches exist which explicitly consider NWFPs as management objectives in forest planning, specific models are needed for the assessment of their production potential in different environmental contexts and for different management regimes. Empirical data supporting a comprehensive assessment of the potential of NWFPs are rare, thus making development of statistical models particularly problematic. However, the complex causal relationships between the sustained production of NWFPs, the available ecological resources, as well as the organizational and the market potential of forest management regimes are well suited for knowledge-based expert models. Bayesian belief networks (BBNs) are a kind of probabilistic graphical model that have become very popular to practitioners and scientists mainly due to the powerful probability theory involved, which makes BBNs suitable to deal with a wide range of environmental problems. In this contribution we present the development of a Bayesian belief network to assess the potential of NWFPs for small scale forest owners. A three stage iterative process with stakeholder and expert participation was used to develop the Bayesian Network within the frame of the StarTree Project. The group of participants varied in the stages of the modelling process. A core team, consisting of one technical expert and two domain experts was responsible for the entire modelling process as well as for the first prototype of the network

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

Energy Technology Data Exchange (ETDEWEB)

Xiao, H., E-mail: hengxiao@vt.edu; Wu, J.-L.; Wang, J.-X.; Sun, R.; Roy, C.J.

2016-11-01

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach

Bayesian ensemble refinement by replica simulations and reweighting

Science.gov (United States)

Hummer, Gerhard; Köfinger, Jürgen

2015-12-01

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir; Al-Naffouri, Tareq Y.

2013-01-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics

A risk management process for reinforced concrete structures by coupling modelling, monitoring and Bayesian approaches

International Nuclear Information System (INIS)

Capra, Bruno; Li, Kefei; Wolff, Valentin; Bernard, Olivier; Gerard, Bruno

2004-01-01

The impact of steel corrosion on the durability of reinforced concrete structures has since a long time been a major concern in civil engineering. The main electrochemical mechanisms of the steel corrosion are know well known. The material and structure degradation is attributed to the progressive formation of an expansive corrosion product at the steel-concrete interface. To assess quantitatively the structure lifetime, a two-stage service life model has been accepted widely. So far, the research attention is mainly given to the corrosion in an un-cracked concrete. However. practically one is often confronted to the reinforcement corrosion in an already cracked concrete. How to quantify the corrosion risk is of great interest for the long term durability of these cracked structures. To this end, this paper proposes a service life modeling for the corrosion process by carbonation in a cracked or un-cracked concrete depending on the observation or monitoring data available. Some recent experimental investigations are used to calibrate the models. Then, the models are applied to a shell structure to quantify the corrosion process and determine the optimal maintenance strategy. As corrosion processes are very difficult to model and subjected to material and environmental random variations, an example of structure reassessment is presented taking into account in situ information by the mean of Bayesian approaches. The coupling of monitoring, modelling and updating leads to a new global maintenance strategy of infrastructure. In conclusion: This paper presents an unified methodology coupling predictive models, observations and Bayesian approaches in order to assess the degradation degree of an ageing structure. The particular case of corrosion is treated on an innovative way by the development of a service life model taking into account cracking effects on the kinetics of the phenomena. At a material level, the dominant factors are the crack opening and the crack nature

Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation

DEFF Research Database (Denmark)

Brouwer, Thomas; Frellsen, Jes; Liò, Pietro

We present a fast variational Bayesian algorithm for performing non-negative matrix factorisation and tri-factorisation. We show that our approach achieves faster convergence per iteration and timestep (wall-clock) than Gibbs sampling and non-probabilistic approaches, and do not require additional...... samples to estimate the posterior. We show that in particular for matrix tri-factorisation convergence is difficult, but our variational Bayesian approach offers a fast solution, allowing the tri-factorisation approach to be used more effectively....

A nonparametric Bayesian approach for genetic evaluation in ...

African Journals Online (AJOL)

Unknown

Finally, one can report the whole of the posterior probability distributions of the parameters in ... the Markov Chain Monte Carlo Methods, and more specific Gibbs Sampling, these ...... Bayesian Methods in Animal Breeding Theory. J. Anim. Sci.

Multimethod, multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves.

Science.gov (United States)

Jiménez, José; García, Emilio J; Llaneza, Luis; Palacios, Vicente; González, Luis Mariano; García-Domínguez, Francisco; Múñoz-Igualada, Jaime; López-Bao, José Vicente

2016-08-01

In many cases, the first step in large-carnivore management is to obtain objective, reliable, and cost-effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical-site-occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost-effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well-coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population-parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

Science.gov (United States)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

Empirical Study on Conservative and Representative Heuristics of Hong Kong Small Investors Adopting Momentum and Contrarian Trading Strategies

OpenAIRE

Chow, Sheung-Chi; Hon, Tai-Yuen; Wong, Wing-Keung; Woo, Kai-Yin

2017-01-01

Recently, a new Bayesian approach has been developed to explain some market anomalies. In this paper, we conduct a questionnaire survey to examine whether the theory holds empirically by studying the conservative and representative heuristics by Hong Kong small investors who adopt momentum and/or contrarian trading strategies. In addition, our study provides evidence for the small investors on their time horizon and risk tolerance when facing uncertainty in their investments. Our findings are...

A Bayesian Decision-Theoretic Approach to Logically-Consistent Hypothesis Testing

Directory of Open Access Journals (Sweden)

Gustavo Miranda da Silva

2015-09-01

Full Text Available This work addresses an important issue regarding the performance of simultaneous test procedures: the construction of multiple tests that at the same time are optimal from a statistical perspective and that also yield logically-consistent results that are easy to communicate to practitioners of statistical methods. For instance, if hypothesis A implies hypothesis B, is it possible to create optimal testing procedures that reject A whenever they reject B? Unfortunately, several standard testing procedures fail in having such logical consistency. Although this has been deeply investigated under a frequentist perspective, the literature lacks analyses under a Bayesian paradigm. In this work, we contribute to the discussion by investigating three rational relationships under a Bayesian decision-theoretic standpoint: coherence, invertibility and union consonance. We characterize and illustrate through simple examples optimal Bayes tests that fulfill each of these requisites separately. We also explore how far one can go by putting these requirements together. We show that although fairly intuitive tests satisfy both coherence and invertibility, no Bayesian testing scheme meets the desiderata as a whole, strengthening the understanding that logical consistency cannot be combined with statistical optimality in general. Finally, we associate Bayesian hypothesis testing with Bayes point estimation procedures. We prove the performance of logically-consistent hypothesis testing by means of a Bayes point estimator to be optimal only under very restrictive conditions.

Prediction of community prevalence of human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach.

Science.gov (United States)

Carabin, Hélène; Escalona, Marisela; Marshall, Clare; Vivas-Martínez, Sarai; Botto, Carlos; Joseph, Lawrence; Basáñez, María-Gloria

2003-01-01

To develop a Bayesian hierarchical model for human onchocerciasis with which to explore the factors that influence prevalence of microfilariae in the Amazonian focus of onchocerciasis and predict the probability of any community being at least mesoendemic (>20% prevalence of microfilariae), and thus in need of priority ivermectin treatment. Models were developed with data from 732 individuals aged > or =15 years who lived in 29 Yanomami communities along four rivers of the south Venezuelan Orinoco basin. The models' abilities to predict prevalences of microfilariae in communities were compared. The deviance information criterion, Bayesian P-values, and residual values were used to select the best model with an approximate cross-validation procedure. A three-level model that acknowledged clustering of infection within communities performed best, with host age and sex included at the individual level, a river-dependent altitude effect at the community level, and additional clustering of communities along rivers. This model correctly classified 25/29 (86%) villages with respect to their need for priority ivermectin treatment. Bayesian methods are a flexible and useful approach for public health research and control planning. Our model acknowledges the clustering of infection within communities, allows investigation of links between individual- or community-specific characteristics and infection, incorporates additional uncertainty due to missing covariate data, and informs policy decisions by predicting the probability that a new community is at least mesoendemic.

Estimation of under-reported visceral Leishmaniasis (Vl cases in Bihar: a Bayesian approach

Directory of Open Access Journals (Sweden)

A Ranjan

2013-12-01

Full Text Available Background: Visceral leishmaniasis (VL is a major health problem in the state of Bihar and adjoining areas in India. In absence of any active surveillance mechanism for the disease, there seems to be gross under-reporting of VL cases. Objective: The objective of this study was to estimate extent of under-reporting of VL cases in Bihar using pooled analysis of published papers. Method: We calculated the pooled common ratio (RRMH based on three studies and combined it with a prior distribution of ratio using inverse-variance weighting method. Bayesian method was used to estimate the posterior distribution of the “under-reporting factor” (ratio of unreported to reported cases. Results: The posterior distribution of ratio of unreported to reported cases yielded a mean of 3.558, with 95% posterior limits of 2.81 and 4.50. Conclusion: Bayesian approach gives evidence to the fact that the total number of VL cases in the state may be nearly more than three times that of currently reported figures. 

Mechanistic curiosity will not kill the Bayesian cat

NARCIS (Netherlands)

Borsboom, D.; Wagenmakers, E.-J.; Romeijn, J.-W.

2011-01-01

Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer

Mechanistic curiosity will not kill the Bayesian cat

NARCIS (Netherlands)

Borsboom, Denny; Wagenmakers, Eric-Jan; Romeijn, Jan-Willem

Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer

Non-homogeneous dynamic Bayesian networks for continuous data

NARCIS (Netherlands)

Grzegorczyk, Marco; Husmeier, Dirk

Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with

Bayesian models a statistical primer for ecologists

CERN Document Server

Hobbs, N Thompson

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 probabili

Bayesian image restoration, using configurations

OpenAIRE

Thorarinsdottir, Thordis

2006-01-01

In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the re...

Bayesian inference for psychology. Part I : Theoretical advantages and practical ramifications

NARCIS (Netherlands)

Wagenmakers, E.-J.; Marsman, M.; Jamil, T.; Ly, A.; Verhagen, J.; Love, J.; Selker, R.; Gronau, Q.F.; Šmíra, M.; Epskamp, S.; Matzke, D.; Rouder, J.N.; Morey, R.D.

2018-01-01

Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete

Editorial: Bayesian benefits for child psychology and psychiatry researchers.

Science.gov (United States)

Oldehinkel, Albertine J

2016-09-01

For many scientists, performing statistical tests has become an almost automated routine. However, p-values are frequently used and interpreted incorrectly; and even when used appropriately, p-values tend to provide answers that do not match researchers' questions and hypotheses well. Bayesian statistics present an elegant and often more suitable alternative. The Bayesian approach has rarely been applied in child psychology and psychiatry research so far, but the development of user-friendly software packages and tutorials has placed it well within reach now. Because Bayesian analyses require a more refined definition of hypothesized probabilities of possible outcomes than the classical approach, going Bayesian may offer the additional benefit of sparkling the development and refinement of theoretical models in our field. © 2016 Association for Child and Adolescent Mental Health.

A new approach for supply chain risk management: Mapping SCOR into Bayesian network

Directory of Open Access Journals (Sweden)

Mahdi Abolghasemi

2015-01-01

Full Text Available Purpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks. Design/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs and supply chain operations reference (SCOR in which making decision on uncertain variables will be done by predictive and diagnostic capabilities. Findings: After applying the proposed method in one of the biggest automotive companies in Iran, we identified key factors of supply chain performance based on SCOR model through predictive and diagnostic capability of Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’ and its criteria that include costs of labors, warranty, transportation and inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations. Research limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some

Application of a naive Bayesians classifiers in assessing the supplier

Directory of Open Access Journals (Sweden)

Mijailović Snežana

2017-01-01

Full Text Available The paper considers the class of interactive knowledge based systems whose main purpose of making proposals and assisting customers in making decisions. The mathematical model provides a set of examples of learning about the delivered series of outflows from three suppliers, as well as an analysis of an illustrative example for assessing the supplier using a naive Bayesian classifier. The model was developed on the basis of the analysis of subjective probabilities, which are later revised with the help of new empirical information and Bayesian theorem on a posterior probability, i.e. combining of subjective and objective conditional probabilities in the choice of a reliable supplier.

Peering through a dirty window: A Bayesian approach to making mine detection decisions from noisy data

Energy Technology Data Exchange (ETDEWEB)

Kercel, Stephen W.

1998-10-11

For several reasons, Bayesian parameter estimation is superior to other methods for extracting features of a weak signal from noise. Since it exploits prior knowledge, the analysis begins from a more advantageous starting point than other methods. Also, since ''nuisance parameters'' can be dropped out of the Bayesian analysis, the description of the model need not be as complete as is necessary for such methods as matched filtering. In the limit for perfectly random noise and a perfect description of the model, the signal-to-noise ratio improves as the square root of the number of samples in the data. Even with the imperfections of real-world data, Bayesian approaches this ideal limit of performance more closely than other methods. A major unsolved problem in landmine detection is the fusion of data from multiple sensor types. Bayesian data fusion is only beginning to be explored as a solution to the problem. In single sensor processes Bayesian analysis can sense multiple parameters from the data stream of the one sensor. It does so by computing a joint probability density function of a set of parameter values from the sensor output. However, there is no inherent requirement that the information must come from a single sensor. If multiple sensors are applied to a single process, where several different parameters are implicit in each sensor output data stream, the joint probability density function of all the parameters of interest can be computed in exactly the same manner as the single sensor case. Thus, it is just as practical to base decisions on multiple sensor outputs as it is for single sensors. This should provide a practical way to combine the outputs of dissimilar sensors, such as ground penetrating radar and electromagnetic induction devices, producing a better detection decision than could be provided by either sensor alone.

Peering through a dirty window: A Bayesian approach to making mine detection decisions from noisy data

International Nuclear Information System (INIS)

Kercel, Stephen W.

1998-01-01

For several reasons, Bayesian parameter estimation is superior to other methods for extracting features of a weak signal from noise. Since it exploits prior knowledge, the analysis begins from a more advantageous starting point than other methods. Also, since ''nuisance parameters'' can be dropped out of the Bayesian analysis, the description of the model need not be as complete as is necessary for such methods as matched filtering. In the limit for perfectly random noise and a perfect description of the model, the signal-to-noise ratio improves as the square root of the number of samples in the data. Even with the imperfections of real-world data, Bayesian approaches this ideal limit of performance more closely than other methods. A major unsolved problem in landmine detection is the fusion of data from multiple sensor types. Bayesian data fusion is only beginning to be explored as a solution to the problem. In single sensor processes Bayesian analysis can sense multiple parameters from the data stream of the one sensor. It does so by computing a joint probability density function of a set of parameter values from the sensor output. However, there is no inherent requirement that the information must come from a single sensor. If multiple sensors are applied to a single process, where several different parameters are implicit in each sensor output data stream, the joint probability density function of all the parameters of interest can be computed in exactly the same manner as the single sensor case. Thus, it is just as practical to base decisions on multiple sensor outputs as it is for single sensors. This should provide a practical way to combine the outputs of dissimilar sensors, such as ground penetrating radar and electromagnetic induction devices, producing a better detection decision than could be provided by either sensor alone

Bayesian methods to estimate urban growth potential

Science.gov (United States)

Smith, Jordan W.; Smart, Lindsey S.; Dorning, Monica; Dupéy, Lauren Nicole; Méley, Andréanne; Meentemeyer, Ross K.

2017-01-01

Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth.

Approaches at KAIST NICIE Lab to quantifying situation awareness in nuclear power plant MCRs

International Nuclear Information System (INIS)

Seong, Poong-Hyun; Yim, Ho-Bin; Kim, Man-Cheol; Ha, Jun-Su; Lee, Hyun-Chul; Lee, Seung-Woo

2013-01-01

Situation awareness (SA) continues to receive a considerable amount of attention from the ergonomics community. But, techniques to measure SA have normally used expert judgment or a self-rating method so far. One of the problems of these techniques is inconsistency of results. So, empirical and analytical studies on an objective and quantitative SA measurement methods have been carried out at Nuclear I and C and Information Engineering Lab (NICIE Lab) in Korea Advanced Institute of Science and Technology (KAIST). Empirical studies are based on physical and cognitive human behaviors, whereas analytical studies are mainly based on Bayesian inference. Eye movement signals and verbal protocol analysis were used for empirical approaches to obtain objective measures. FIR and SAE measures showed feasibility of an eye-tracking method for robust application. TSA score based on a verbal protocol analysis also showed its possibility of team SA (TSA) quantification. Bayesian inference was used for analytical approaches of SA quantification. The analytical quantification method was further expanded to consider some of the important human properties, and a SA modelling tool called ‘CoRSAGE, ver01” was developed. (author)

The Theoretical and Empirical Approaches to the Definition of Audit Risk

Directory of Open Access Journals (Sweden)

Berezhniy Yevgeniy B.

2017-12-01

Full Text Available The risk category is one of the key factors in planning the audit and assessing its results. The article is aimed at generalizing the theoretical and empirical approaches to the definition of audit risk and methods of its reduction. The structure of audit risk was analyzed and it has been determined, that each of researchers approached to structuring of audit risk from the subjective point of view. The author’s own model of audit risk has been proposed. The basic methods of assessment of audit risk are generalized, the theoretical and empirical approaches to its definition are allocated, also it is noted, that application of any of the given models can be suitable rather for approximate estimation, than for exact calculation of an audit risk, as it is accompanied by certain shortcomings.

Bayesian estimation and tracking a practical guide

CERN Document Server

Haug, Anton J

2012-01-01

A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation

Bayesian community detection

DEFF Research Database (Denmark)

Mørup, Morten; Schmidt, Mikkel N

2012-01-01

Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....

Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

Science.gov (United States)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

2016-05-01

Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert

Learning Bayesian Networks with Incomplete Data by Augmentation

OpenAIRE

Adel, Tameem; de Campos, Cassio P.

2016-01-01

We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...

On Data and Parameter Estimation Using the Variational Bayesian EM-algorithm for Block-fading Frequency-selective MIMO Channels

DEFF Research Database (Denmark)

Christensen, Lars P.B.; Larsen, Jan

2006-01-01

A general Variational Bayesian framework for iterative data and parameter estimation for coherent detection is introduced as a generalization of the EM-algorithm. Explicit solutions are given for MIMO channel estimation with Gaussian prior and noise covariance estimation with inverse-Wishart prior....... Simulation of a GSM-like system provides empirical proof that the VBEM-algorithm is able to provide better performance than the EM-algorithm. However, if the posterior distribution is highly peaked, the VBEM-algorithm approaches the EM-algorithm and the gain disappears. The potential gain is therefore...

The Bayesian Approach to Association

Science.gov (United States)

Arora, N. S.

2017-12-01

The Bayesian approach to Association focuses mainly on quantifying the physics of the domain. In the case of seismic association for instance let X be the set of all significant events (above some threshold) and their attributes, such as location, time, and magnitude, Y1 be the set of detections that are caused by significant events and their attributes such as seismic phase, arrival time, amplitude etc., Y2 be the set of detections that are not caused by significant events, and finally Y be the set of observed detections We would now define the joint distribution P(X, Y1, Y2, Y) = P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2) ; where the last term simply states that Y1 and Y2 are a partitioning of Y. Given the above joint distribution the inference problem is simply to find the X, Y1, and Y2 that maximizes posterior probability P(X, Y1, Y2| Y) which reduces to maximizing P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2). In this expression P(X) captures our prior belief about event locations. P(Y1 | X) captures notions of travel time, residual error distributions as well as detection and mis-detection probabilities. While P(Y2) captures the false detection rate of our seismic network. The elegance of this approach is that all of the assumptions are stated clearly in the model for P(X), P(Y1|X) and P(Y2). The implementation of the inference is merely a by-product of this model. In contrast some of the other methods such as GA hide a number of assumptions in the implementation details of the inference - such as the so called "driver cells." The other important aspect of this approach is that all seismic knowledge including knowledge from other domains such as infrasound and hydroacoustic can be included in the same model. So, we don't need to separately account for misdetections or merge seismic and infrasound events as a separate step. Finally, it should be noted that the objective of automatic association is to simplify the job of humans who are publishing seismic bulletins based on this

A Bayesian Justification for Random Sampling in Sample Survey

Directory of Open Access Journals (Sweden)

Glen Meeden

2012-07-01

Full Text Available In the usual Bayesian approach to survey sampling the sampling design, plays a minimal role, at best. Although a close relationship between exchangeable prior distributions and simple random sampling has been noted; how to formally integrate simple random sampling into the Bayesian paradigm is not clear. Recently it has been argued that the sampling design can be thought of as part of a Bayesian's prior distribution. We will show here that under this scenario simple random sample can be given a Bayesian justification in survey sampling.

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

Bayesian approach to inverse statistical mechanics

Science.gov (United States)

Habeck, Michael

2014-05-01

Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.

Personalized Audio Systems - a Bayesian Approach

DEFF Research Database (Denmark)

Nielsen, Jens Brehm; Jensen, Bjørn Sand; Hansen, Toke Jansen

2013-01-01

Modern audio systems are typically equipped with several user-adjustable parameters unfamiliar to most users listening to the system. To obtain the best possible setting, the user is forced into multi-parameter optimization with respect to the users's own objective and preference. To address this......, the present paper presents a general inter-active framework for personalization of such audio systems. The framework builds on Bayesian Gaussian process regression in which a model of the users's objective function is updated sequentially. The parameter setting to be evaluated in a given trial is selected...

Bayesian Approach to Spectral Function Reconstruction for Euclidean Quantum Field Theories

Science.gov (United States)

Burnier, Yannis; Rothkopf, Alexander

2013-11-01

We present a novel approach to the inference of spectral functions from Euclidean time correlator data that makes close contact with modern Bayesian concepts. Our method differs significantly from the maximum entropy method (MEM). A new set of axioms is postulated for the prior probability, leading to an improved expression, which is devoid of the asymptotically flat directions present in the Shanon-Jaynes entropy. Hyperparameters are integrated out explicitly, liberating us from the Gaussian approximations underlying the evidence approach of the maximum entropy method. We present a realistic test of our method in the context of the nonperturbative extraction of the heavy quark potential. Based on hard-thermal-loop correlator mock data, we establish firm requirements in the number of data points and their accuracy for a successful extraction of the potential from lattice QCD. Finally we reinvestigate quenched lattice QCD correlators from a previous study and provide an improved potential estimation at T=2.33TC.

Evidence on Features of a DSGE Business Cycle Model from Bayesian Model Averaging

NARCIS (Netherlands)

R.W. Strachan (Rodney); H.K. van Dijk (Herman)

2012-01-01

textabstractThe empirical support for features of a Dynamic Stochastic General Equilibrium model with two technology shocks is valuated using Bayesian model averaging over vector autoregressions. The model features include equilibria, restrictions on long-run responses, a structural break of unknown

A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

Science.gov (United States)

Thomsen, Nanna I.; Binning, Philip J.; McKnight, Ursula S.; Tuxen, Nina; Bjerg, Poul L.; Troldborg, Mads

2016-05-01

A key component in risk assessment of contaminated sites is in the formulation of a conceptual site model (CSM). A CSM is a simplified representation of reality and forms the basis for the mathematical modeling of contaminant fate and transport at the site. The CSM should therefore identify the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert opinion at different knowledge levels. The developed BBNs combine data from desktop studies and initial site investigations with expert opinion to assess which of the CSMs are more likely to reflect the actual site conditions. The method is demonstrated on a Danish field site, contaminated with chlorinated ethenes. Four different CSMs are developed by combining two contaminant source zone interpretations (presence or absence of a separate phase contamination) and two geological interpretations (fractured or unfractured clay till). The beliefs in each of the CSMs are assessed sequentially based on data from three investigation stages (a screening investigation, a more detailed investigation, and an expert consultation) to demonstrate that the belief can be updated as more information

Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments.

Science.gov (United States)

Kaplan, David; Lee, Chansoon

2018-01-01

This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model's posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

Approximation methods for efficient learning of Bayesian networks

CERN Document Server

Riggelsen, C

2008-01-01

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

Bayesian emulation for optimization in multi-step portfolio decisions

OpenAIRE

Irie, Kaoru; West, Mike

2016-01-01

We discuss the Bayesian emulation approach to computational solution of multi-step portfolio studies in financial time series. "Bayesian emulation for decisions" involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portf...

Generalized least squares and empirical Bayes estimation in regional partial duration series index-flood modeling

DEFF Research Database (Denmark)

Madsen, Henrik; Rosbjerg, Dan

1997-01-01

parameters is inferred from regional data using generalized least squares (GLS) regression. Two different Bayesian T-year event estimators are introduced: a linear estimator that requires only some moments of the prior distributions to be specified and a parametric estimator that is based on specified......A regional estimation procedure that combines the index-flood concept with an empirical Bayes method for inferring regional information is introduced. The model is based on the partial duration series approach with generalized Pareto (GP) distributed exceedances. The prior information of the model...

Towards Multi-Method Research Approach in Empirical Software Engineering

Science.gov (United States)

Mandić, Vladimir; Markkula, Jouni; Oivo, Markku

This paper presents results of a literature analysis on Empirical Research Approaches in Software Engineering (SE). The analysis explores reasons why traditional methods, such as statistical hypothesis testing and experiment replication are weakly utilized in the field of SE. It appears that basic assumptions and preconditions of the traditional methods are contradicting the actual situation in the SE. Furthermore, we have identified main issues that should be considered by the researcher when selecting the research approach. In virtue of reasons for weak utilization of traditional methods we propose stronger use of Multi-Method approach with Pragmatism as the philosophical standpoint.

Bayesian Population Physiologically-Based Pharmacokinetic (PBPK Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations.

Directory of Open Access Journals (Sweden)

Markus Krauss

Full Text Available Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to

Bayesian molecular dating: opening up the black box.

Science.gov (United States)

Bromham, Lindell; Duchêne, Sebastián; Hua, Xia; Ritchie, Andrew M; Duchêne, David A; Ho, Simon Y W

2018-05-01

Molecular dating analyses allow evolutionary timescales to be estimated from genetic data, offering an unprecedented capacity for investigating the evolutionary past of all species. These methods require us to make assumptions about the relationship between genetic change and evolutionary time, often referred to as a 'molecular clock'. Although initially regarded with scepticism, molecular dating has now been adopted in many areas of biology. This broad uptake has been due partly to the development of Bayesian methods that allow complex aspects of molecular evolution, such as variation in rates of change across lineages, to be taken into account. But in order to do this, Bayesian dating methods rely on a range of assumptions about the evolutionary process, which vary in their degree of biological realism and empirical support. These assumptions can have substantial impacts on the estimates produced by molecular dating analyses. The aim of this review is to open the 'black box' of Bayesian molecular dating and have a look at the machinery inside. We explain the components of these dating methods, the important decisions that researchers must make in their analyses, and the factors that need to be considered when interpreting results. We illustrate the effects that the choices of different models and priors can have on the outcome of the analysis, and suggest ways to explore these impacts. We describe some major research directions that may improve the reliability of Bayesian dating. The goal of our review is to help researchers to make informed choices when using Bayesian phylogenetic methods to estimate evolutionary rates and timescales. © 2017 Cambridge Philosophical Society.

A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

International Nuclear Information System (INIS)

Cai, C.; Rodet, T.; Mohammad-Djafari, A.; Legoupil, S.

2013-01-01

Purpose: Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images.Methods: This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed.Results: The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

Science.gov (United States)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

A default Bayesian hypothesis test for mediation.

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Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan

2015-03-01

In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

Science.gov (United States)

Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

2011-01-01

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

Bayesianism and inference to the best explanation

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Valeriano IRANZO

2008-01-01

Full Text Available Bayesianism and Inference to the best explanation (IBE are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” (IBE-Bay and “frequentist-Bayesianism” (Freq-Bay. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: (i endorses a role for explanatory value in the assessment of scientific hypotheses; (ii avoids a purely subjectivist reading of prior probabilities; and (iii fits better than IBE-Bayesianism with two basic facts about science, i.e., the prominent role played by empirical testing and the existence of many scientific theories in the past that failed to fulfil their promises and were subsequently abandoned.

Evaluating a Bayesian approach to improve accuracy of individual photographic identification methods using ecological distribution data

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Richard Stafford

2011-04-01

Full Text Available Photographic identification of individual organisms can be possible from natural body markings. Data from photo-ID can be used to estimate important ecological and conservation metrics such as population sizes, home ranges or territories. However, poor quality photographs or less well-studied individuals can result in a non-unique ID, potentially confounding several similar looking individuals. Here we present a Bayesian approach that uses known data about previous sightings of individuals at specific sites as priors to help assess the problems of obtaining a non-unique ID. Using a simulation of individuals with different confidence of correct ID we evaluate the accuracy of Bayesian modified (posterior probabilities. However, in most cases, the accuracy of identification decreases. Although this technique is unsuccessful, it does demonstrate the importance of computer simulations in testing such hypotheses in ecology.

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...

Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach.

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Fernandes, G S; Bhattacharya, A; McWilliams, D F; Ingham, S L; Doherty, M; Zhang, W

2017-03-20

Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort. A total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ 2 statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model. A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in primary care to predict the risk of knee pain in the general population.

National evaluation of Chinese coastal erosion to sea level rise using a Bayesian approach

International Nuclear Information System (INIS)

Zhan, Q; Fan, X; Du, X; Zhu, J

2014-01-01

In this paper a Causal Bayesian network is developed to predict decadal-scale shoreline evolution of China to sea-level rise. The Bayesian model defines relationships between 6 factors of Chinese coastal system such as coastal geomorphology, mean tide range, mean wave height, coastal slope, relative sea-level rise rate and shoreline erosion rate. Using the Bayesian probabilistic model, we make quantitative assessment of china's shoreline evolution in response to different future sea level rise rates. Results indicate that the probability of coastal erosion with high and very high rates increases from 28% to 32.3% when relative sea-level rise rates is 4∼6mm/a, and to 44.9% when relative sea-level rise rates is more than 6mm/a. A hindcast evaluation of the Bayesian model shows that the model correctly predicts 79.3% of the cases. Model test indicates that the Bayesian model shows higher predictive capabilities for stable coasts and very highly eroding coasts than moderately and highly eroding coasts. This study demonstrates that the Bayesian model is adapted to predicting decadal-scale Chinese coastal erosion associated with sea-level rise

Bayesian outcome-based strategy classification.

Science.gov (United States)

Lee, Michael D

2016-03-01

Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the widely-used "weighted additive" (WADD) model, and show how this model, as well as other important models like "take-the-best" (TTB), can and should be expressed in terms of meaningful priors. Methodologically, they develop an inference approach based on the Minimum Description Length (MDL) principles that balances both the goodness-of-fit and complexity of the decision models they consider. This paper aims to preserve these useful contributions, but provide a complementary Bayesian approach with some theoretical and methodological advantages. We develop a simple graphical model, implemented in JAGS, that allows for fully Bayesian inferences about which models people use to make decisions. To demonstrate the Bayesian approach, we apply it to the models and data considered by Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014), showing how a prior predictive analysis of the models, and posterior inferences about which models people use and the parameter settings at which they use them, can contribute to our understanding of human decision making.

Constructive Verification, Empirical Induction, and Falibilist Deduction: A Threefold Contrast

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Julio Michael Stern

2011-10-01

Full Text Available This article explores some open questions related to the problem of verification of theories in the context of empirical sciences by contrasting three epistemological frameworks. Each of these epistemological frameworks is based on a corresponding central metaphor, namely: (a Neo-empiricism and the gambling metaphor; (b Popperian falsificationism and the scientific tribunal metaphor; (c Cognitive constructivism and the object as eigen-solution metaphor. Each of one of these epistemological frameworks has also historically co-evolved with a certain statistical theory and method for testing scientific hypotheses, respectively: (a Decision theoretic Bayesian statistics and Bayes factors; (b Frequentist statistics and p-values; (c Constructive Bayesian statistics and e-values. This article examines with special care the Zero Probability Paradox (ZPP, related to the verification of sharp or precise hypotheses. Finally, this article makes some remarks on Lakatos’ view of mathematics as a quasi-empirical science.

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

Estimating the occurrence of foreign material in Advanced Gas-cooled Reactors: A Bayesian Monte Carlo approach

International Nuclear Information System (INIS)

Mason, Paolo

2014-01-01

Highlights: • The amount of a specific type of foreign material found in UK AGRs has been estimated. • The estimate is based on very few instances of detection in numerous inspections. • A Bayesian Monte Carlo approach was used. • The study supports safety case claims on coolant flow impairment. • The methodology is applicable to any inspection campaign on any plant system. - Abstract: The current occurrence of a particular sort of foreign material in eight UK Advanced Gas-cooled Reactors has been estimated by means of a parametric approach. The study includes both variability, treated in analytic fashion via the combination of standard probability distributions, and the uncertainty in the parameters of the model of choice, whose posterior distribution was inferred in Bayesian fashion by means of a Monte Carlo route consisting in the conditional acceptance of sets of model parameters drawn from a prior distribution based on engineering judgement. The model underlying the present study specifically refers to the re-loading and inspection routines of UK Advanced Gas-cooled Reactors. The approach to inference here presented, however, is of general validity and can be applied to the outcome of any inspection campaign on any plant system, and indeed to any situation in which the outcome of a stochastic process is more easily simulated than described by a probability density or mass function

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

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Michael Jae-Yoon Chung

Full Text Available A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i learn probabilistic models of actions through self-discovery and experience, (ii utilize these learned models for inferring the goals of human actions, and (iii perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i a simulated robot that learns human-like gaze following behavior, and (ii a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

Bayesian analysis of systems with random chemical composition: renormalization-group approach to Dirichlet distributions and the statistical theory of dilution.

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Vlad, Marcel Ovidiu; Tsuchiya, Masa; Oefner, Peter; Ross, John

2002-01-01

We investigate the statistical properties of systems with random chemical composition and try to obtain a theoretical derivation of the self-similar Dirichlet distribution, which is used empirically in molecular biology, environmental chemistry, and geochemistry. We consider a system made up of many chemical species and assume that the statistical distribution of the abundance of each chemical species in the system is the result of a succession of a variable number of random dilution events, which can be described by using the renormalization-group theory. A Bayesian approach is used for evaluating the probability density of the chemical composition of the system in terms of the probability densities of the abundances of the different chemical species. We show that for large cascades of dilution events, the probability density of the composition vector of the system is given by a self-similar probability density of the Dirichlet type. We also give an alternative formal derivation for the Dirichlet law based on the maximum entropy approach, by assuming that the average values of the chemical potentials of different species, expressed in terms of molar fractions, are constant. Although the maximum entropy approach leads formally to the Dirichlet distribution, it does not clarify the physical origin of the Dirichlet statistics and has serious limitations. The random theory of dilution provides a physical picture for the emergence of Dirichlet statistics and makes it possible to investigate its validity range. We discuss the implications of our theory in molecular biology, geochemistry, and environmental science.

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.

2012-01-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical

Evaluating Vegetation Potential for Wildfire Impacted Watershed Using a Bayesian Network Modeling Approach

Science.gov (United States)

Jaramillo, L. V.; Stone, M. C.; Morrison, R. R.

2017-12-01

Decision-making for natural resource management is complex especially for fire impacted watersheds in the Southwestern US because of the vital importance of water resources, exorbitant cost of fire management and restoration, and the risks of the wildland-urban interface (WUI). While riparian and terrestrial vegetation are extremely important to ecosystem health and provide ecosystem services, loss of vegetation due to wildfire, post-fire flooding, and debris flows can lead to further degradation of the watershed and increased vulnerability to erosion and debris flow. Land managers are charged with taking measures to mitigate degradation of the watershed effectively and efficiently with limited time, money, and data. For our study, a Bayesian network (BN) approach is implemented to understand vegetation potential for Kashe-Katuwe Tent Rocks National Monument in the fire-impacted Peralta Canyon Watershed, New Mexico, USA. We implement both two-dimensional hydrodynamic and Bayesian network modeling to incorporate spatial variability in the system. Our coupled modeling framework presents vegetation recruitment and succession potential for three representative plant types (native riparian, native terrestrial, and non-native) under several hydrologic scenarios and management actions. In our BN model, we use variables that address timing, hydrologic, and groundwater conditions as well as recruitment and succession constraints for the plant types based on expert knowledge and literature. Our approach allows us to utilize small and incomplete data, incorporate expert knowledge, and explicitly account for uncertainty in the system. Our findings can be used to help land managers and local decision-makers determine their plan of action to increase watershed health and resilience.

A Bayesian inverse modeling approach to estimate soil hydraulic properties of a toposequence in southeastern Amazonia.

Science.gov (United States)

Stucchi Boschi, Raquel; Qin, Mingming; Gimenez, Daniel; Cooper, Miguel

2016-04-01

Modeling is an important tool for better understanding and assessing land use impacts on landscape processes. A key point for environmental modeling is the knowledge of soil hydraulic properties. However, direct determination of soil hydraulic properties is difficult and costly, particularly in vast and remote regions such as one constituting the Amazon Biome. One way to overcome this problem is to extrapolate accurately estimated data to pedologically similar sites. The van Genuchten (VG) parametric equation is the most commonly used for modeling SWRC. The use of a Bayesian approach in combination with the Markov chain Monte Carlo to estimate the VG parameters has several advantages compared to the widely used global optimization techniques. The Bayesian approach provides posterior distributions of parameters that are independent from the initial values and allow for uncertainty analyses. The main objectives of this study were: i) to estimate hydraulic parameters from data of pasture and forest sites by the Bayesian inverse modeling approach; and ii) to investigate the extrapolation of the estimated VG parameters to a nearby toposequence with pedologically similar soils to those used for its estimate. The parameters were estimated from volumetric water content and tension observations obtained after rainfall events during a 207-day period from pasture and forest sites located in the southeastern Amazon region. These data were used to run HYDRUS-1D under a Differential Evolution Adaptive Metropolis (DREAM) scheme 10,000 times, and only the last 2,500 times were used to calculate the posterior distributions of each hydraulic parameter along with 95% confidence intervals (CI) of volumetric water content and tension time series. Then, the posterior distributions were used to generate hydraulic parameters for two nearby toposequences composed by six soil profiles, three are under forest and three are under pasture. The parameters of the nearby site were accepted when

A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.

Science.gov (United States)

Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.

1997-03-01

There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Comparison of Bayesian and frequentist approaches in modelling risk of preterm birth near the Sydney Tar Ponds, Nova Scotia, Canada

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Canty Angelo

2007-09-01

Full Text Available Abstract Background This study compares the Bayesian and frequentist (non-Bayesian approaches in the modelling of the association between the risk of preterm birth and maternal proximity to hazardous waste and pollution from the Sydney Tar Pond site in Nova Scotia, Canada. Methods The data includes 1604 observed cases of preterm birth out of a total population of 17559 at risk of preterm birth from 144 enumeration districts in the Cape Breton Regional Municipality. Other covariates include the distance from the Tar Pond; the rate of unemployment to population; the proportion of persons who are separated, divorced or widowed; the proportion of persons who have no high school diploma; the proportion of persons living alone; the proportion of single parent families and average income. Bayesian hierarchical Poisson regression, quasi-likelihood Poisson regression and weighted linear regression models were fitted to the data. Results The results of the analyses were compared together with their limitations. Conclusion The results of the weighted linear regression and the quasi-likelihood Poisson regression agrees with the result from the Bayesian hierarchical modelling which incorporates the spatial effects.

BELM: Bayesian extreme learning machine.

Science.gov (United States)

Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J

2011-03-01

The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.

Bayesian statistical inference

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Bruno De Finetti

2017-04-01

Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.

Bayesian Exponential Smoothing.

OpenAIRE

Forbes, C.S.; Snyder, R.D.; Shami, R.S.

2000-01-01

In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows us to improve current practices surrounding exponential smoothing by providing both point predictions and measures of the uncertainty surrounding them.

Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

Science.gov (United States)

Levy, Roy

2014-01-01

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

Bayesian models: A statistical primer for ecologists

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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

Embedding the results of focussed Bayesian fusion into a global context

Science.gov (United States)

Sander, Jennifer; Heizmann, Michael

2014-05-01

Bayesian statistics offers a well-founded and powerful fusion methodology also for the fusion of heterogeneous information sources. However, except in special cases, the needed posterior distribution is not analytically derivable. As consequence, Bayesian fusion may cause unacceptably high computational and storage costs in practice. Local Bayesian fusion approaches aim at reducing the complexity of the Bayesian fusion methodology significantly. This is done by concentrating the actual Bayesian fusion on the potentially most task relevant parts of the domain of the Properties of Interest. Our research on these approaches is motivated by an analogy to criminal investigations where criminalists pursue clues also only locally. This publication follows previous publications on a special local Bayesian fusion technique called focussed Bayesian fusion. Here, the actual calculation of the posterior distribution gets completely restricted to a suitably chosen local context. By this, the global posterior distribution is not completely determined. Strategies for using the results of a focussed Bayesian analysis appropriately are needed. In this publication, we primarily contrast different ways of embedding the results of focussed Bayesian fusion explicitly into a global context. To obtain a unique global posterior distribution, we analyze the application of the Maximum Entropy Principle that has been shown to be successfully applicable in metrology and in different other areas. To address the special need for making further decisions subsequently to the actual fusion task, we further analyze criteria for decision making under partial information.

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

Quantitative methods in ethnobotany and ethnopharmacology: considering the overall flora--hypothesis testing for over- and underused plant families with the Bayesian approach.

Science.gov (United States)

Weckerle, Caroline S; Cabras, Stefano; Castellanos, Maria Eugenia; Leonti, Marco

2011-09-01

We introduce and explain the advantages of the Bayesian approach and exemplify the method with an analysis of the medicinal flora of Campania, Italy. The Bayesian approach is a new method, which allows to compare medicinal floras with the overall flora of a given area and to investigate over- and underused plant families. In contrast to previously used methods (regression analysis and binomial method) it considers the inherent uncertainty around the analyzed data. The medicinal flora with 423 species was compiled based on nine studies on local medicinal plant use in Campania. The total flora comprises 2237 species belonging to 128 families. Statistical analysis was performed with the Bayesian method and the binomial method. An approximated χ(2)-test was used to analyze the relationship between use categories and higher taxonomic groups. Among the larger plant families we find the Lamiaceae, Rosaceae, and Malvaceae, to be overused in the local medicine of Campania and the Orchidaceae, Caryophyllaceae, Poaceae, and Fabaceae to be underused compared to the overall flora. Furthermore, do specific medicinal uses tend to be correlated with taxonomic plant groups. For example, are the Monocots heavily used for urological complaints. Testing for over- and underused taxonomic groups of a flora with the Bayesian method is easy to adopt and can readily be calculated in excel spreadsheets using the excel function Inverse beta (INV.BETA). In contrast to the binomial method the presented method is also suitable for small datasets. With larger datasets the two methods tend to converge. However, results are generally more conservative with the Bayesian method pointing out fewer families as over- or underused. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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.

Exploiting neurovascular coupling: a Bayesian sequential Monte Carlo approach applied to simulated EEG fNIRS data

Science.gov (United States)

Croce, Pierpaolo; Zappasodi, Filippo; Merla, Arcangelo; Chiarelli, Antonio Maria

2017-08-01

Objective. Electrical and hemodynamic brain activity are linked through the neurovascular coupling process and they can be simultaneously measured through integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Thanks to the lack of electro-optical interference, the two procedures can be easily combined and, whereas EEG provides electrophysiological information, fNIRS can provide measurements of two hemodynamic variables, such as oxygenated and deoxygenated hemoglobin. A Bayesian sequential Monte Carlo approach (particle filter, PF) was applied to simulated recordings of electrical and neurovascular mediated hemodynamic activity, and the advantages of a unified framework were shown. Approach. Multiple neural activities and hemodynamic responses were simulated in the primary motor cortex of a subject brain. EEG and fNIRS recordings were obtained by means of forward models of volume conduction and light propagation through the head. A state space model of combined EEG and fNIRS data was built and its dynamic evolution was estimated through a Bayesian sequential Monte Carlo approach (PF). Main results. We showed the feasibility of the procedure and the improvements in both electrical and hemodynamic brain activity reconstruction when using the PF on combined EEG and fNIRS measurements. Significance. The investigated procedure allows one to combine the information provided by the two methodologies, and, by taking advantage of a physical model of the coupling between electrical and hemodynamic response, to obtain a better estimate of brain activity evolution. Despite the high computational demand, application of such an approach to in vivo recordings could fully exploit the advantages of this combined brain imaging technology.

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

A Bayesian account of quantum histories

International Nuclear Information System (INIS)

Marlow, Thomas

2006-01-01

We investigate whether quantum history theories can be consistent with Bayesian reasoning and whether such an analysis helps clarify the interpretation of such theories. First, we summarise and extend recent work categorising two different approaches to formalising multi-time measurements in quantum theory. The standard approach consists of describing an ordered series of measurements in terms of history propositions with non-additive 'probabilities.' The non-standard approach consists of defining multi-time measurements to consist of sets of exclusive and exhaustive history propositions and recovering the single-time exclusivity of results when discussing single-time history propositions. We analyse whether such history propositions can be consistent with Bayes' rule. We show that certain class of histories are given a natural Bayesian interpretation, namely, the linearly positive histories originally introduced by Goldstein and Page. Thus, we argue that this gives a certain amount of interpretational clarity to the non-standard approach. We also attempt a justification of our analysis using Cox's axioms of probability theory

A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction

Science.gov (United States)

De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David

2016-01-01

Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. PMID:25281847

A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction.

Science.gov (United States)

De Oliveira Martins, Leonardo; Mallo, Diego; Posada, David

2016-05-01

Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society of Systematic Biologists.

Bayesian probabilistic network approach for managing earthquake risks of cities

DEFF Research Database (Denmark)

Bayraktarli, Yahya; Faber, Michael

2011-01-01

This paper considers the application of Bayesian probabilistic networks (BPNs) to large-scale risk based decision making in regard to earthquake risks. A recently developed risk management framework is outlined which utilises Bayesian probabilistic modelling, generic indicator based risk models...... and a fourth module on the consequences of an earthquake. Each of these modules is integrated into a BPN. Special attention is given to aggregated risk, i.e. the risk contribution from assets at multiple locations in a city subjected to the same earthquake. The application of the methodology is illustrated...... on an example considering a portfolio of reinforced concrete structures in a city located close to the western part of the North Anatolian Fault in Turkey....

Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations

Energy Technology Data Exchange (ETDEWEB)

Chen, Peng, E-mail: peng@ices.utexas.edu [The Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th Street, Stop C0200, Austin, TX 78712-1229 (United States); Schwab, Christoph, E-mail: christoph.schwab@sam.math.ethz.ch [Seminar für Angewandte Mathematik, Eidgenössische Technische Hochschule, Römistrasse 101, CH-8092 Zürich (Switzerland)

2016-07-01

We extend the reduced basis (RB) accelerated Bayesian inversion methods for affine-parametric, linear operator equations which are considered in [16,17] to non-affine, nonlinear parametric operator equations. We generalize the analysis of sparsity of parametric forward solution maps in [20] and of Bayesian inversion in [48,49] to the fully discrete setting, including Petrov–Galerkin high-fidelity (“HiFi”) discretization of the forward maps. We develop adaptive, stochastic collocation based reduction methods for the efficient computation of reduced bases on the parametric solution manifold. The nonaffinity and nonlinearity with respect to (w.r.t.) the distributed, uncertain parameters and the unknown solution is collocated; specifically, by the so-called Empirical Interpolation Method (EIM). For the corresponding Bayesian inversion problems, computational efficiency is enhanced in two ways: first, expectations w.r.t. the posterior are computed by adaptive quadratures with dimension-independent convergence rates proposed in [49]; the present work generalizes [49] to account for the impact of the PG discretization in the forward maps on the convergence rates of the Quantities of Interest (QoI for short). Second, we propose to perform the Bayesian estimation only w.r.t. a parsimonious, RB approximation of the posterior density. Based on the approximation results in [49], the infinite-dimensional parametric, deterministic forward map and operator admit N-term RB and EIM approximations which converge at rates which depend only on the sparsity of the parametric forward map. In several numerical experiments, the proposed algorithms exhibit dimension-independent convergence rates which equal, at least, the currently known rate estimates for N-term approximation. We propose to accelerate Bayesian estimation by first offline construction of reduced basis surrogates of the Bayesian posterior density. The parsimonious surrogates can then be employed for online data

A Bayesian Network approach to the evaluation of building design and its consequences for employee performance and operational costs

DEFF Research Database (Denmark)

Jensen, Kasper Lynge; Toftum, Jørn; Friis-Hansen, Peter

2009-01-01

A Bayesian Network approach has been developed that can compare different building designs by estimating the effects of the thermal indoor environment on the mental performance of office workers. A part of this network is based on the compilation of subjective thermal sensation data and the assoc...

A population-based Bayesian approach to the minimal model of glucose and insulin homeostasis

DEFF Research Database (Denmark)

Andersen, Kim Emil; Højbjerre, Malene

2005-01-01

-posed estimation problem, where the reconstruction most often has been done by non-linear least squares techniques separately for each entity. The minmal model was originally specified for a single individual and does not combine several individuals with the advantage of estimating the metabolic portrait...... to a population-based model. The estimation of the parameters are efficiently implemented in a Bayesian approach where posterior inference is made through the use of Markov chain Monte Carlo techniques. Hereby we obtain a powerful and flexible modelling framework for regularizing the ill-posed estimation problem...

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.

An efficient multiple particle filter based on the variational Bayesian approach

KAUST Repository

Ait-El-Fquih, Boujemaa

2015-12-07

This paper addresses the filtering problem in large-dimensional systems, in which conventional particle filters (PFs) remain computationally prohibitive owing to the large number of particles needed to obtain reasonable performances. To overcome this drawback, a class of multiple particle filters (MPFs) has been recently introduced in which the state-space is split into low-dimensional subspaces, and then a separate PF is applied to each subspace. In this paper, we adopt the variational Bayesian (VB) approach to propose a new MPF, the VBMPF. The proposed filter is computationally more efficient since the propagation of each particle requires generating one (new) particle only, while in the standard MPFs a set of (children) particles needs to be generated. In a numerical test, the proposed VBMPF behaves better than the PF and MPF.

On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood.

Science.gov (United States)

Karabatsos, George

2018-06-01

This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together. The new method is illustrated through a test of the cancellation axioms on a classic survey data set, and through the analysis of simulated data.

On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.

Science.gov (United States)

Gomez-Ramirez, Jaime; Sanz, Ricardo

2013-09-01

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist. Copyright © 2013 Elsevier Ltd. All rights reserved.

Assessment of successful smoking cessation by psychological factors using the Bayesian network approach.

Science.gov (United States)

Yang, Xiaorong; Li, Suyun; Pan, Lulu; Wang, Qiang; Li, Huijie; Han, Mingkui; Zhang, Nan; Jiang, Fan; Jia, Chongqi

2016-07-01

The association between psychological factors and smoking cessation is complicated and inconsistent in published researches, and the joint effect of psychological factors on smoking cessation is unclear. This study explored how psychological factors jointly affect the success of smoking cessation using a Bayesian network approach. A community-based case control study was designed with 642 adult male successful smoking quitters as the cases, and 700 adult male failed smoking quitters as the controls. General self-efficacy (GSE), trait coping style (positive-trait coping style (PTCS) and negative-trait coping style (NTCS)) and self-rating anxiety (SA) were evaluated by GSE Scale, Trait Coping Style Questionnaire and SA Scale, respectively. Bayesian network was applied to evaluate the relationship between psychological factors and successful smoking cessation. The local conditional probability table of smoking cessation indicated that different joint conditions of psychological factors led to different outcomes for smoking cessation. Among smokers with high PTCS, high NTCS and low SA, only 36.40% successfully quitted smoking. However, among smokers with low pack-years of smoking, high GSE, high PTCS and high SA, 63.64% successfully quitted smoking. Our study indicates psychological factors jointly influence smoking cessation outcome. According to different joint situations, different solutions should be developed to control tobacco in practical intervention.

Dynamic probability evaluation of safety levels of earth-rockfill dams using Bayesian approach

Directory of Open Access Journals (Sweden)

Zi-wu Fan

2009-06-01

Full Text Available In order to accurately predict and control the aging process of dams, new information should be collected continuously to renew the quantitative evaluation of dam safety levels. Owing to the complex structural characteristics of dams, it is quite difficult to predict the time-varying factors affecting their safety levels. It is not feasible to employ dynamic reliability indices to evaluate the actual safety levels of dams. Based on the relevant regulations for dam safety classification in China, a dynamic probability description of dam safety levels was developed. Using the Bayesian approach and effective information mining, as well as real-time information, this study achieved more rational evaluation and prediction of dam safety levels. With the Bayesian expression of discrete stochastic variables, the a priori probabilities of the dam safety levels determined by experts were combined with the likelihood probability of the real-time check information, and the probability information for the evaluation of dam safety levels was renewed. The probability index was then applied to dam rehabilitation decision-making. This method helps reduce the difficulty and uncertainty of the evaluation of dam safety levels and complies with the current safe decision-making regulations for dams in China. It also enhances the application of current risk analysis methods for dam safety levels.

An empirical approach to wisdom processes

Directory of Open Access Journals (Sweden)

Ioana Laura Dumbravă

2017-12-01

Full Text Available In psychology, wisdom gradually received more and more interest form researchers. The first who have directed their attention to this area were the Greek philosophers, but gradually theoretical models were developed based on empirical data and components that explain the development of wisdom. In this paper, we used Ardelt's approach, which takes into account both the eastern and western approaches regarding wisdom. Within a sample of students (N = 100, mean age = 24, SD = 6.44, wisdom was investigated in relation to several phenomena that could be involved in the development of wisdom and wisdom processes. Thus, wisdom is studied in relation to general metacognition, moral metacognition, cognitive empathy, emotional empathy, irrationality and cognitive flexibility. The results were found to be in accordance with the hypothesis of affective empathy, identifying a significant positive relationship between the two variables. One must consider that the rest of the correlations were negative and significant, thus drawing attention to other possible factors that might be important in describing wisdom. Variables evaluated were found to explain the variance of wisdom.

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…

Bayesian Graphical Models

DEFF Research Database (Denmark)

Jensen, Finn Verner; Nielsen, Thomas Dyhre

2016-01-01

Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...

Predicting Drug Safety and Communicating Risk: Benefits of a Bayesian Approach.

Science.gov (United States)

Lazic, Stanley E; Edmunds, Nicholas; Pollard, Christopher E

2018-03-01

Drug toxicity is a major source of attrition in drug discovery and development. Pharmaceutical companies routinely use preclinical data to predict clinical outcomes and continue to invest in new assays to improve predictions. However, there are many open questions about how to make the best use of available data, combine diverse data, quantify risk, and communicate risk and uncertainty to enable good decisions. The costs of suboptimal decisions are clear: resources are wasted and patients may be put at risk. We argue that Bayesian methods provide answers to all of these problems and use hERG-mediated QT prolongation as a case study. Benefits of Bayesian machine learning models include intuitive probabilistic statements of risk that incorporate all sources of uncertainty, the option to include diverse data and external information, and visualizations that have a clear link between the output from a statistical model and what this means for risk. Furthermore, Bayesian methods are easy to use with modern software, making their adoption for safety screening straightforward. We include R and Python code to encourage the adoption of these methods.

BAYESIAN APPROACH TO THE PROCESS OF IDENTIFICATION OF THE DETERMINANTS OF INNOVATIVENESS

Directory of Open Access Journals (Sweden)

Marta Czyżewska

2014-08-01

Full Text Available Bayesian belief networks are applied in determining the most important factors of the innovativeness level of national economies. The paper is divided into two parts. The first presentsthe basic theory of Bayesian networks whereas in the second, the belief networks have been generated by an inhouse developed computer system called BeliefSEEKER which was implemented to generate the determinants influencing the innovativeness level of national economies.Qualitative analysis of the generated belief networks provided a way to define a set of the most important dimensions influencing the innovativeness level of economies and then the indicators that form these dimensions. It has been proven that Bayesian networks are very effective methods for multidimensional analysis and forming conclusions and recommendations regarding the strength of each innovative determinant influencing the overall performance of a country’s economy.

Three Insights from a Bayesian Interpretation of the One-Sided "P" Value

Science.gov (United States)

Marsman, Maarten; Wagenmakers, Eric-Jan

2017-01-01

P values have been critiqued on several grounds but remain entrenched as the dominant inferential method in the empirical sciences. In this article, we elaborate on the fact that in many statistical models, the one-sided "P" value has a direct Bayesian interpretation as the approximate posterior mass for values lower than zero. The…

A Bayesian Hierarchical Modeling Approach to Predicting Flow in Ungauged Basins

Science.gov (United States)

Gronewold, A.; Alameddine, I.; Anderson, R. M.

2009-12-01

Recent innovative approaches to identifying and applying regression-based relationships between land use patterns (such as increasing impervious surface area and decreasing vegetative cover) and rainfall-runoff model parameters represent novel and promising improvements to predicting flow from ungauged basins. In particular, these approaches allow for predicting flows under uncertain and potentially variable future conditions due to rapid land cover changes, variable climate conditions, and other factors. Despite the broad range of literature on estimating rainfall-runoff model parameters, however, the absence of a robust set of modeling tools for identifying and quantifying uncertainties in (and correlation between) rainfall-runoff model parameters represents a significant gap in current hydrological modeling research. Here, we build upon a series of recent publications promoting novel Bayesian and probabilistic modeling strategies for quantifying rainfall-runoff model parameter estimation uncertainty. Our approach applies alternative measures of rainfall-runoff model parameter joint likelihood (including Nash-Sutcliffe efficiency, among others) to simulate samples from the joint parameter posterior probability density function. We then use these correlated samples as response variables in a Bayesian hierarchical model with land use coverage data as predictor variables in order to develop a robust land use-based tool for forecasting flow in ungauged basins while accounting for, and explicitly acknowledging, parameter estimation uncertainty. We apply this modeling strategy to low-relief coastal watersheds of Eastern North Carolina, an area representative of coastal resource waters throughout the world because of its sensitive embayments and because of the abundant (but currently threatened) natural resources it hosts. Consequently, this area is the subject of several ongoing studies and large-scale planning initiatives, including those conducted through the United

Integrating health economics modeling in the product development cycle of medical devices: a Bayesian approach.

Science.gov (United States)

Vallejo-Torres, Laura; Steuten, Lotte M G; Buxton, Martin J; Girling, Alan J; Lilford, Richard J; Young, Terry

2008-01-01

Medical device companies are under growing pressure to provide health-economic evaluations of their products. Cost-effectiveness analyses are commonly undertaken as a one-off exercise at the late stage of development of new technologies; however, the benefits of an iterative use of economic evaluation during the development process of new products have been acknowledged in the literature. Furthermore, the use of Bayesian methods within health technology assessment has been shown to be of particular value in the dynamic framework of technology appraisal when new information becomes available in the life cycle of technologies. In this study, we set out a methodology to adapt these methods for their application to directly support investment decisions in a commercial setting from early stages of the development of new medical devices. Starting with relatively simple analysis from the very early development phase and proceeding to greater depth of analysis at later stages, a Bayesian approach facilitates the incorporation of all available evidence and would help companies to make better informed choices at each decision point.

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.

Empirical data and moral theory. A plea for integrated empirical ethics.

Science.gov (United States)

Molewijk, Bert; Stiggelbout, Anne M; Otten, Wilma; Dupuis, Heleen M; Kievit, Job

2004-01-01

Ethicists differ considerably in their reasons for using empirical data. This paper presents a brief overview of four traditional approaches to the use of empirical data: "the prescriptive applied ethicists," "the theorists," "the critical applied ethicists," and "the particularists." The main aim of this paper is to introduce a fifth approach of more recent date (i.e. "integrated empirical ethics") and to offer some methodological directives for research in integrated empirical ethics. All five approaches are presented in a table for heuristic purposes. The table consists of eight columns: "view on distinction descriptive-prescriptive sciences," "location of moral authority," "central goal(s)," "types of normativity," "use of empirical data," "method," "interaction empirical data and moral theory," and "cooperation with descriptive sciences." Ethicists can use the table in order to identify their own approach. Reflection on these issues prior to starting research in empirical ethics should lead to harmonization of the different scientific disciplines and effective planning of the final research design. Integrated empirical ethics (IEE) refers to studies in which ethicists and descriptive scientists cooperate together continuously and intensively. Both disciplines try to integrate moral theory and empirical data in order to reach a normative conclusion with respect to a specific social practice. IEE is not wholly prescriptive or wholly descriptive since IEE assumes an interdepence between facts and values and between the empirical and the normative. The paper ends with three suggestions for consideration on some of the future challenges of integrated empirical ethics.

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.

Bayesian adaptive methods for clinical trials

National Research Council Canada - National Science Library

Berry, Scott M

2011-01-01

.... One is that Bayesian approaches implemented with the majority of their informative content coming from the current data, and not any external prior informa- tion, typically have good frequentist properties (e.g...

CytoBayesJ: software tools for Bayesian analysis of cytogenetic radiation dosimetry data.

Science.gov (United States)

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

2013-08-30

A number of authors have suggested that a Bayesian approach may be most appropriate for analysis of cytogenetic radiation dosimetry data. In the Bayesian framework, probability of an event is described in terms of previous expectations and uncertainty. Previously existing, or prior, information is used in combination with experimental results to infer probabilities or the likelihood that a hypothesis is true. It has been shown that the Bayesian approach increases both the accuracy and quality assurance of radiation dose estimates. New software entitled CytoBayesJ has been developed with the aim of bringing Bayesian analysis to cytogenetic biodosimetry laboratory practice. CytoBayesJ takes a number of Bayesian or 'Bayesian like' methods that have been proposed in the literature and presents them to the user in the form of simple user-friendly tools, including testing for the most appropriate model for distribution of chromosome aberrations and calculations of posterior probability distributions. The individual tools are described in detail and relevant examples of the use of the methods and the corresponding CytoBayesJ software tools are given. In this way, the suitability of the Bayesian approach to biological radiation dosimetry is highlighted and its wider application encouraged by providing a user-friendly software interface and manual in English and Russian. Copyright © 2013 Elsevier B.V. All rights reserved.

Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors

OpenAIRE

Rens van de Schoot; Joris J. Broere; Koen H. Perryck; Mariëlle Zondervan-Zwijnenburg; Nancy E. van Loey

2015-01-01

Background: The analysis of small data sets in longitudinal studies can lead to power issues and often suffers from biased parameter values. These issues can be solved by using Bayesian estimation in conjunction with informative prior distributions. By means of a simulation study and an empirical example concerning posttraumatic stress symptoms (PTSS) following mechanical ventilation in burn survivors, we demonstrate the advantages and potential pitfalls of using Bayesian estimation.Methods: ...

A Robust Obstacle Avoidance for Service Robot Using Bayesian Approach

Directory of Open Access Journals (Sweden)

Widodo Budiharto

2011-03-01

Full Text Available The objective of this paper is to propose a robust obstacle avoidance method for service robot in indoor environment. The method for obstacles avoidance uses information about static obstacles on the landmark using edge detection. Speed and direction of people that walks as moving obstacle obtained by single camera using tracking and recognition system and distance measurement using 3 ultrasonic sensors. A new geometrical model and maneuvering method for moving obstacle avoidance introduced and combined with Bayesian approach for state estimation. The obstacle avoidance problem is formulated using decision theory, prior and posterior distribution and loss function to determine an optimal response based on inaccurate sensor data. Algorithms for moving obstacles avoidance method proposed and experiment results implemented to service robot also presented. Various experiments show that our proposed method very fast, robust and successfully implemented to service robot called Srikandi II that equipped with 4 DOF arm robot developed in our laboratory.

Prediction of road accidents: A Bayesian hierarchical approach.

Science.gov (United States)

Deublein, Markus; Schubert, Matthias; Adey, Bryan T; Köhler, Jochen; Faber, Michael H

2013-03-01

In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any

Human dental age estimation using third molar developmental stages: does a Bayesian approach outperform regression models to discriminate between juveniles and adults?

Science.gov (United States)

Thevissen, P W; Fieuws, S; Willems, G

2010-01-01

Dental age estimation methods based on the radiologically detected third molar developmental stages are implemented in forensic age assessments to discriminate between juveniles and adults considering the judgment of young unaccompanied asylum seekers. Accurate and unbiased age estimates combined with appropriate quantified uncertainties are the required properties for accurate forensic reporting. In this study, a subset of 910 individuals uniformly distributed in age between 16 and 22 years was selected from an existing dataset collected by Gunst et al. containing 2,513 panoramic radiographs with known third molar developmental stages of Belgian Caucasian men and women. This subset was randomly split in a training set to develop a classical regression analysis and a Bayesian model for the multivariate distribution of the third molar developmental stages conditional on age and in a test set to assess the performance of both models. The aim of this study was to verify if the Bayesian approach differentiates the age of maturity more precisely and removes the bias, which disadvantages the systematically overestimated young individuals. The Bayesian model offers the discrimination of subjects being older than 18 years more appropriate and produces more meaningful prediction intervals but does not strongly outperform the classical approaches.

A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions

International Nuclear Information System (INIS)

Brito, Mario; Griffiths, Gwyn

2016-01-01

Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability and environmental factors, and cannot be determined through analytical means alone. An alternative approach – formal expert judgment – is a time-consuming process; consequently a method is needed to broaden the applicability of judgments beyond the narrow confines of an elicitation for a defined environment. We propose and explore a solution founded on a Bayesian Belief Network (BBN), where the results of the expert judgment elicitation are taken as the initial prior probability of loss due to failure. The network topology captures the causal effects of the environment separately on the vehicle and on the support platform, and combines these to produce an updated probability of loss due to failure. An extended version of the Kaplan–Meier estimator is then used to update the mission risk profile with travelled distance. Sensitivity analysis of the BBN is presented and a case study of Autosub3 AUV deployment in the Amundsen Sea is discussed in detail. - Highlights: • Novel method to estimate risk of autonomous vehicle loss in uncertain environments. • A framework to integrate frequentist and subjective probability modelling. • A Bayesian belief updating method for capturing variation in operating environment. • Graphical approach for sensitivity analysis, applicable to any BBN model validation. • Pragmatic case studies showing the application of the proposed framework.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Science.gov (United States)

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Directory of Open Access Journals (Sweden)

Hashem Salarzadeh Jenatabadi

Full Text Available Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Software Health Management with Bayesian Networks

Science.gov (United States)

Mengshoel, Ole; Schumann, JOhann

2011-01-01

Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.

Validation of Sustainable Development Practices Scale Using the Bayesian Approach to Item Response Theory

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Martin Hernani Merino

2014-12-01

Full Text Available There has been growing recognition of the importance of creating performance measurement tools for the economic, social and environmental management of micro and small enterprise (MSE. In this context, this study aims to validate an instrument to assess perceptions of sustainable development practices by MSEs by means of a Graded Response Model (GRM with a Bayesian approach to Item Response Theory (IRT. The results based on a sample of 506 university students in Peru, suggest that a valid measurement instrument was achieved. At the end of the paper, methodological and managerial contributions are presented.

A tutorial introduction to Bayesian models of cognitive development.

Science.gov (United States)

Perfors, Amy; Tenenbaum, Joshua B; Griffiths, Thomas L; Xu, Fei

2011-09-01

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science. Copyright © 2010 Elsevier B.V. All rights reserved.

Bayesian inference in probabilistic risk assessment-The current state of the art

International Nuclear Information System (INIS)

Kelly, Dana L.; Smith, Curtis L.

2009-01-01

Markov chain Monte Carlo (MCMC) approaches to sampling directly from the joint posterior distribution of aleatory model parameters have led to tremendous advances in Bayesian inference capability in a wide variety of fields, including probabilistic risk analysis. The advent of freely available software coupled with inexpensive computing power has catalyzed this advance. This paper examines where the risk assessment community is with respect to implementing modern computational-based Bayesian approaches to inference. Through a series of examples in different topical areas, it introduces salient concepts and illustrates the practical application of Bayesian inference via MCMC sampling to a variety of important problems

Bayesian data analysis in population ecology: motivations, methods, and benefits

Science.gov (United States)

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

Airline Sustainability Modeling: A New Framework with Application of Bayesian Structural Equation Modeling

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Hashem Salarzadeh Jenatabadi

2016-11-01

Full Text Available There are many factors which could influence the sustainability of airlines. The main purpose of this study is to introduce a framework for a financial sustainability index and model it based on structural equation modeling (SEM with maximum likelihood and Bayesian predictors. The introduced framework includes economic performance, operational performance, cost performance, and financial performance. Based on both Bayesian SEM (Bayesian-SEM and Classical SEM (Classical-SEM, it was found that economic performance with both operational performance and cost performance are significantly related to the financial performance index. The four mathematical indices employed are root mean square error, coefficient of determination, mean absolute error, and mean absolute percentage error to compare the efficiency of Bayesian-SEM and Classical-SEM in predicting the airline financial performance. The outputs confirmed that the framework with Bayesian prediction delivered a good fit with the data, although the framework predicted with a Classical-SEM approach did not prepare a well-fitting model. The reasons for this discrepancy between Classical and Bayesian predictions, as well as the potential advantages and caveats with the application of Bayesian approach in airline sustainability studies, are debated.

On the prior probabilities for two-stage Bayesian estimates

International Nuclear Information System (INIS)

Kohut, P.

1992-01-01

The method of Bayesian inference is reexamined for its applicability and for the required underlying assumptions in obtaining and using prior probability estimates. Two different approaches are suggested to determine the first-stage priors in the two-stage Bayesian analysis which avoid certain assumptions required for other techniques. In the first scheme, the prior is obtained through a true frequency based distribution generated at selected intervals utilizing actual sampling of the failure rate distributions. The population variability distribution is generated as the weighed average of the frequency distributions. The second method is based on a non-parametric Bayesian approach using the Maximum Entropy Principle. Specific features such as integral properties or selected parameters of prior distributions may be obtained with minimal assumptions. It is indicated how various quantiles may also be generated with a least square technique

Empirical and Bayesian approaches to fossil-only divergence times: A study across three reptile clades.

Science.gov (United States)

Turner, Alan H; Pritchard, Adam C; Matzke, Nicholas J

2017-01-01

Estimating divergence times on phylogenies is critical in paleontological and neontological studies. Chronostratigraphically-constrained fossils are the only direct evidence of absolute timing of species divergence. Strict temporal calibration of fossil-only phylogenies provides minimum divergence estimates, and various methods have been proposed to estimate divergences beyond these minimum values. We explore the utility of simultaneous estimation of tree topology and divergence times using BEAST tip-dating on datasets consisting only of fossils by using relaxed morphological clocks and birth-death tree priors that include serial sampling (BDSS) at a constant rate through time. We compare BEAST results to those from the traditional maximum parsimony (MP) and undated Bayesian inference (BI) methods. Three overlapping datasets were used that span 250 million years of archosauromorph evolution leading to crocodylians. The first dataset focuses on early Sauria (31 taxa, 240 chars.), the second on early Archosauria (76 taxa, 400 chars.) and the third on Crocodyliformes (101 taxa, 340 chars.). For each dataset three time-calibrated trees (timetrees) were calculated: a minimum-age timetree with node ages based on earliest occurrences in the fossil record; a 'smoothed' timetree using a range of time added to the root that is then averaged over zero-length internodes; and a tip-dated timetree. Comparisons within datasets show that the smoothed and tip-dated timetrees provide similar estimates. Only near the root node do BEAST estimates fall outside the smoothed timetree range. The BEAST model is not able to overcome limited sampling to correctly estimate divergences considerably older than sampled fossil occurrence dates. Conversely, the smoothed timetrees consistently provide node-ages far older than the strict dates or BEAST estimates for morphologically conservative sister-taxa when they sit on long ghost lineages. In this latter case, the relaxed-clock model appears to

Empirical and Bayesian approaches to fossil-only divergence times: A study across three reptile clades.

Directory of Open Access Journals (Sweden)

Alan H Turner

Full Text Available Estimating divergence times on phylogenies is critical in paleontological and neontological studies. Chronostratigraphically-constrained fossils are the only direct evidence of absolute timing of species divergence. Strict temporal calibration of fossil-only phylogenies provides minimum divergence estimates, and various methods have been proposed to estimate divergences beyond these minimum values. We explore the utility of simultaneous estimation of tree topology and divergence times using BEAST tip-dating on datasets consisting only of fossils by using relaxed morphological clocks and birth-death tree priors that include serial sampling (BDSS at a constant rate through time. We compare BEAST results to those from the traditional maximum parsimony (MP and undated Bayesian inference (BI methods. Three overlapping datasets were used that span 250 million years of archosauromorph evolution leading to crocodylians. The first dataset focuses on early Sauria (31 taxa, 240 chars., the second on early Archosauria (76 taxa, 400 chars. and the third on Crocodyliformes (101 taxa, 340 chars.. For each dataset three time-calibrated trees (timetrees were calculated: a minimum-age timetree with node ages based on earliest occurrences in the fossil record; a 'smoothed' timetree using a range of time added to the root that is then averaged over zero-length internodes; and a tip-dated timetree. Comparisons within datasets show that the smoothed and tip-dated timetrees provide similar estimates. Only near the root node do BEAST estimates fall outside the smoothed timetree range. The BEAST model is not able to overcome limited sampling to correctly estimate divergences considerably older than sampled fossil occurrence dates. Conversely, the smoothed timetrees consistently provide node-ages far older than the strict dates or BEAST estimates for morphologically conservative sister-taxa when they sit on long ghost lineages. In this latter case, the relaxed

Pilot study of dynamic Bayesian networks approach for fault diagnostics and accident progression prediction in HTR-PM

Energy Technology Data Exchange (ETDEWEB)

Zhao, Yunfei; Tong, Jiejuan; Zhang, Liguo, E-mail: lgzhang@tsinghua.edu.cn; Zhang, Qin

2015-09-15

Highlights: • Dynamic Bayesian network is used to diagnose and predict accident progress in HTR-PM. • Dynamic Bayesian network model of HTR-PM is built based on detailed system analysis. • LOCA Simulations validate the above model even if part monitors are lost or false. - Abstract: The first high-temperature-reactor pebble-bed demonstration module (HTR-PM) is under construction currently in China. At the same time, development of a system that is used to support nuclear emergency response is in progress. The supporting system is expected to complete two tasks. The first one is diagnostics of the fault in the reactor based on abnormal sensor measurements obtained. The second one is prognostic of the accident progression based on sensor measurements obtained and operator actions. Both tasks will provide valuable guidance for emergency staff to take appropriate protective actions. Traditional method for the two tasks relies heavily on expert judgment, and has been proven to be inappropriate in some cases, such as Three Mile Island accident. To better perform the two tasks, dynamic Bayesian networks (DBN) is introduced in this paper and a pilot study based on the approach is carried out. DBN is advantageous in representing complex dynamic systems and taking full consideration of evidences obtained to perform diagnostics and prognostics. Pearl's loopy belief propagation (LBP) algorithm is recommended for diagnostics and prognostics in DBN. The DBN model of HTR-PM is created based on detailed system analysis and accident progression analysis. A small break loss of coolant accident (SBLOCA) is selected to illustrate the application of the DBN model of HTR-PM in fault diagnostics (FD) and accident progression prognostics (APP). Several advantages of DBN approach compared with other techniques are discussed. The pilot study lays the foundation for developing the nuclear emergency response supporting system (NERSS) for HTR-PM.

Physico-empirical approach for mapping soil hydraulic behaviour

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G. D'Urso

1997-01-01

Full Text Available Abstract: Pedo-transfer functions are largely used in soil hydraulic characterisation of large areas. The use of physico-empirical approaches for the derivation of soil hydraulic parameters from disturbed samples data can be greatly enhanced if a characterisation performed on undisturbed cores of the same type of soil is available. In this study, an experimental procedure for deriving maps of soil hydraulic behaviour is discussed with reference to its application in an irrigation district (30 km2 in southern Italy. The main steps of the proposed procedure are: i the precise identification of soil hydraulic functions from undisturbed sampling of main horizons in representative profiles for each soil map unit; ii the determination of pore-size distribution curves from larger disturbed sampling data sets within the same soil map unit. iii the calibration of physical-empirical methods for retrieving soil hydraulic parameters from particle-size data and undisturbed soil sample analysis; iv the definition of functional hydraulic properties from water balance output; and v the delimitation of soil hydraulic map units based on functional properties.

Bayesian hypothesis testing and the maintenance rule

International Nuclear Information System (INIS)

Kelly, D.L.

1997-01-01

The Maintenance Rule (10 CFR 50.65) went into effect in the United States in July 1996. It requires commercial nuclear utilities to monitor system performance (system reliability and maintenance unavailability) for systems that are determined by the utility to be important to plant safety. Utilities must set performance goals for such systems and monitor system performance against these goals. In addition, these performance goals are intended to be commensurate with the safety significance of the system, which can be established by a probabilistic safety assessment of the plant. The author examines the frequents approach to monitoring performance, which is being used by several utilities, and proposes an alternative Bayesian approach. The Bayesian approach makes more complete use of the information in the probabilistic safety assessment, is consistent philosophically with the subjective interpretation given to probability in most probabilistic safety assessments, overcomes several pitfalls in the frequents approach, provides results which are easily interpretable, and is straightforward to implement using the information in the probabilistic safety assessment

Source selection for analogical reasoning an empirical approach

Energy Technology Data Exchange (ETDEWEB)

Stubblefield, W.A. [Sandia National Labs., Albuquerque, NM (United States); Luger, G.F. [Univ. of New Mexico, Albuquerque, NM (United States)

1996-12-31

The effectiveness of an analogical reasoner depends upon its ability to select a relevant analogical source. In many problem domains, however, too little is known about target problems to support effective source selection. This paper describes the design and evaluation of SCAVENGER, an analogical reasoner that applies two techniques to this problem: (1) An assumption-based approach to matching that allows properties of candidate sources to match unknown target properties in the absence of evidence to the contrary. (2) The use of empirical learning to improve memory organization based on problem solving experience.

Bayesian inference method for stochastic damage accumulation modeling

International Nuclear Information System (INIS)

Jiang, Xiaomo; Yuan, Yong; Liu, Xian

2013-01-01

Damage accumulation based reliability model plays an increasingly important role in successful realization of condition based maintenance for complicated engineering systems. This paper developed a Bayesian framework to establish stochastic damage accumulation model from historical inspection data, considering data uncertainty. Proportional hazards modeling technique is developed to model the nonlinear effect of multiple influencing factors on system reliability. Different from other hazard modeling techniques such as normal linear regression model, the approach does not require any distribution assumption for the hazard model, and can be applied for a wide variety of distribution models. A Bayesian network is created to represent the nonlinear proportional hazards models and to estimate model parameters by Bayesian inference with Markov Chain Monte Carlo simulation. Both qualitative and quantitative approaches are developed to assess the validity of the established damage accumulation model. Anderson–Darling goodness-of-fit test is employed to perform the normality test, and Box–Cox transformation approach is utilized to convert the non-normality data into normal distribution for hypothesis testing in quantitative model validation. The methodology is illustrated with the seepage data collected from real-world subway tunnels.

Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory.

Science.gov (United States)

Tauber, Sean; Navarro, Daniel J; Perfors, Amy; Steyvers, Mark

2017-07-01

Recent debates in the psychological literature have raised questions about the assumptions that underpin Bayesian models of cognition and what inferences they license about human cognition. In this paper we revisit this topic, arguing that there are 2 qualitatively different ways in which a Bayesian model could be constructed. The most common approach uses a Bayesian model as a normative standard upon which to license a claim about optimality. In the alternative approach, a descriptive Bayesian model need not correspond to any claim that the underlying cognition is optimal or rational, and is used solely as a tool for instantiating a substantive psychological theory. We present 3 case studies in which these 2 perspectives lead to different computational models and license different conclusions about human cognition. We demonstrate how the descriptive Bayesian approach can be used to answer different sorts of questions than the optimal approach, especially when combined with principled tools for model evaluation and model selection. More generally we argue for the importance of making a clear distinction between the 2 perspectives. Considerable confusion results when descriptive models and optimal models are conflated, and if Bayesians are to avoid contributing to this confusion it is important to avoid making normative claims when none are intended. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Measurement of the Z and W production cross section in pp collisions at LHC using a bayesian approach

CERN Document Server

Ragoni, Simone

The aim of all my work has been to compute the fiducial production cross sections of W± and Z0 bosons in their leptonic (e, µ) decays using the data collected by the ATLAS detector at LHC with a center of mass energy of √s = 13 TeV during summer 2015. The selected events are exactly the same as the ones employed by the recently published article by the ATLAS Collaboration over the same topic, enabling us to compare the obtained results. Necessary comparison, if I may, for the results were obtained with two different procedures: baseline (classical) for the article, bayesian in this thesis. The bayesian approach allows for a natural combination among the many channels and a straightforward treatment of the systematic uncertainties. The obtained results are in excellent agreement with the Standard Model predictions and those published by ATLAS.

Virtue-based Approaches to Professional Ethics: a Plea for More Rigorous Use of Empirical Science

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Georg Spielthenner

2017-08-01

Full Text Available Until recently, the method of professional ethics has been largely principle-based. But the failure of this approach to take into sufficient account the character of professionals has led to a revival of virtue ethics. The kind of professional virtue ethics that I am concerned with in this paper is teleological in that it relates the virtues of a profession to the ends of this profession. My aim is to show how empirical research can (in addition to philosophical inquiry be used to develop virtue-based accounts of professional ethics, and that such empirically well-informed approaches are more convincing than traditional kinds of professional virtue ethics. The paper is divided into four sections. In the first, I outline the structure of a teleological approach to virtue ethics. In Section 2, I show that empirical research can play an essential role in professional ethics by emphasizing the difference between conceptual and empirical matters. Section 3 demonstrates the relevance of virtues in professional life; and the last section is concerned with some meta-ethical issues that are raised by a teleological account of professional virtues.

Bayesian inference for Hawkes processes

DEFF Research Database (Denmark)

Rasmussen, Jakob Gulddahl

The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....

Bayesian inference for Hawkes processes

DEFF Research Database (Denmark)

Rasmussen, Jakob Gulddahl

2013-01-01

The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....

Bayesian Sampling using Condition Indicators

DEFF Research Database (Denmark)

Faber, Michael H.; Sørensen, John Dalsgaard

2002-01-01

of condition indicators introduced by Benjamin and Cornell (1970) a Bayesian approach to quality control is formulated. The formulation is then extended to the case where the quality control is based on sampling of indirect information about the condition of the components, i.e. condition indicators...

Bayesian signal processing classical, modern, and particle filtering methods

CERN Document Server

Candy, James V

2016-01-01

This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters" as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to "fill-in-the gaps" of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical "sanity testing" lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed an...

A 3D model retrieval approach based on Bayesian networks lightfield descriptor

Science.gov (United States)

Xiao, Qinhan; Li, Yanjun

2009-12-01

A new 3D model retrieval methodology is proposed by exploiting a novel Bayesian networks lightfield descriptor (BNLD). There are two key novelties in our approach: (1) a BN-based method for building lightfield descriptor; and (2) a 3D model retrieval scheme based on the proposed BNLD. To overcome the disadvantages of the existing 3D model retrieval methods, we explore BN for building a new lightfield descriptor. Firstly, 3D model is put into lightfield, about 300 binary-views can be obtained along a sphere, then Fourier descriptors and Zernike moments descriptors can be calculated out from binaryviews. Then shape feature sequence would be learned into a BN model based on BN learning algorithm; Secondly, we propose a new 3D model retrieval method by calculating Kullback-Leibler Divergence (KLD) between BNLDs. Beneficial from the statistical learning, our BNLD is noise robustness as compared to the existing methods. The comparison between our method and the lightfield descriptor-based approach is conducted to demonstrate the effectiveness of our proposed methodology.

Empirical Test Case Specification

DEFF Research Database (Denmark)

Kalyanova, Olena; Heiselberg, Per

This document includes the empirical specification on the IEA task of evaluation building energy simulation computer programs for the Double Skin Facades (DSF) constructions. There are two approaches involved into this procedure, one is the comparative approach and another is the empirical one. I....... In the comparative approach the outcomes of different software tools are compared, while in the empirical approach the modelling results are compared with the results of experimental test cases....

An overview on Approximate Bayesian computation*

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Baragatti Meïli

2014-01-01

Full Text Available Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the most satisfactory approach to intractable likelihood problems. This overview presents recent results since its introduction about ten years ago in population genetics.

Bayesian uncertainty analyses of probabilistic risk models

International Nuclear Information System (INIS)

Pulkkinen, U.

1989-01-01

Applications of Bayesian principles to the uncertainty analyses are discussed in the paper. A short review of the most important uncertainties and their causes is provided. An application of the principle of maximum entropy to the determination of Bayesian prior distributions is described. An approach based on so called probabilistic structures is presented in order to develop a method of quantitative evaluation of modelling uncertainties. The method is applied to a small example case. Ideas for application areas for the proposed method are discussed

Parameterization of aquatic ecosystem functioning and its natural variation: Hierarchical Bayesian modelling of plankton food web dynamics

Science.gov (United States)

Norros, Veera; Laine, Marko; Lignell, Risto; Thingstad, Frede

2017-10-01

Methods for extracting empirically and theoretically sound parameter values are urgently needed in aquatic ecosystem modelling to describe key flows and their variation in the system. Here, we compare three Bayesian formulations for mechanistic model parameterization that differ in their assumptions about the variation in parameter values between various datasets: 1) global analysis - no variation, 2) separate analysis - independent variation and 3) hierarchical analysis - variation arising from a shared distribution defined by hyperparameters. We tested these methods, using computer-generated and empirical data, coupled with simplified and reasonably realistic plankton food web models, respectively. While all methods were adequate, the simulated example demonstrated that a well-designed hierarchical analysis can result in the most accurate and precise parameter estimates and predictions, due to its ability to combine information across datasets. However, our results also highlighted sensitivity to hyperparameter prior distributions as an important caveat of hierarchical analysis. In the more complex empirical example, hierarchical analysis was able to combine precise identification of parameter values with reasonably good predictive performance, although the ranking of the methods was less straightforward. We conclude that hierarchical Bayesian analysis is a promising tool for identifying key ecosystem-functioning parameters and their variation from empirical datasets.

Probabilistic forecasting and Bayesian data assimilation

CERN Document Server

Reich, Sebastian

2015-01-01

In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in ap...

Exploring the Influence of Neighborhood Characteristics on Burglary Risks: A Bayesian Random Effects Modeling Approach

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Hongqiang Liu

2016-06-01

Full Text Available A Bayesian random effects modeling approach was used to examine the influence of neighborhood characteristics on burglary risks in Jianghan District, Wuhan, China. This random effects model is essentially spatial; a spatially structured random effects term and an unstructured random effects term are added to the traditional non-spatial Poisson regression model. Based on social disorganization and routine activity theories, five covariates extracted from the available data at the neighborhood level were used in the modeling. Three regression models were fitted and compared by the deviance information criterion to identify which model best fit our data. A comparison of the results from the three models indicates that the Bayesian random effects model is superior to the non-spatial models in fitting the data and estimating regression coefficients. Our results also show that neighborhoods with above average bar density and department store density have higher burglary risks. Neighborhood-specific burglary risks and posterior probabilities of neighborhoods having a burglary risk greater than 1.0 were mapped, indicating the neighborhoods that should warrant more attention and be prioritized for crime intervention and reduction. Implications and limitations of the study are discussed in our concluding section.

Bayesian Inference Methods for Sparse Channel Estimation

DEFF Research Database (Denmark)

Pedersen, Niels Lovmand

2013-01-01

This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

Bayesian image restoration, using configurations

DEFF Research Database (Denmark)

Thorarinsdottir, Thordis

configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed...

A review of the nurtured heart approach to parenting: evaluation of its theoretical and empirical foundations.

Science.gov (United States)

Hektner, Joel M; Brennan, Alison L; Brotherson, Sean E

2013-09-01

The Nurtured Heart Approach to parenting (NHA; Glasser & Easley, 2008) is summarized and evaluated in terms of its alignment with current theoretical perspectives and empirical evidence in family studies and developmental science. Originally conceived and promoted as a behavior management approach for parents of difficult children (i.e., with behavior disorders), NHA is increasingly offered as a valuable strategy for parents of any children, despite a lack of published empirical support. Parents using NHA are trained to minimize attention to undesired behaviors, provide positive attention and praise for compliance with rules, help children be successful by scaffolding and shaping desired behavior, and establish a set of clear rules and consequences. Many elements of the approach have strong support in the theoretical and empirical literature; however, some of the assumptions are more questionable, such as that negative child behavior can always be attributed to unintentional positive reinforcement by parents responding with negative attention. On balance, NHA appears to promote effective and validated parenting practices, but its effectiveness now needs to be tested empirically. © FPI, Inc.

A Bayesian perspective on some replacement strategies

International Nuclear Information System (INIS)

Mazzuchi, Thomas A.; Soyer, Refik

1996-01-01

In this paper we present a Bayesian decision theoretic approach for determining optimal replacement strategies. This approach enables us to formally incorporate, express, and update our uncertainty when determining optimal replacement strategies. We develop relevant expressions for both the block replacement protocol with minimal repair and the age replacement protocol and illustrate the use of our approach with real data

Quantum Bayesian rule for weak measurements of qubits in superconducting circuit QED

International Nuclear Information System (INIS)

Wang, Peiyue; Qin, Lupei; Li, Xin-Qi

2014-01-01

Compared with the quantum trajectory equation (QTE), the quantum Bayesian approach has the advantage of being more efficient to infer a quantum state under monitoring, based on the integrated output of measurements. For weak measurement of qubits in circuit quantum electrodynamics (cQED), properly accounting for the measurement backaction effects within the Bayesian framework is an important problem of current interest. Elegant work towards this task was carried out by Korotkov in ‘bad-cavity’ and weak-response limits (Korotkov 2011 Quantum Bayesian approach to circuit QED measurement (arXiv:1111.4016)). In the present work, based on insights from the cavity-field states (dynamics) and the help of an effective QTE, we generalize the results of Korotkov to more general system parameters. The obtained Bayesian rule is in full agreement with Korotkov's result in limiting cases and as well holds satisfactory accuracy in non-limiting cases in comparison with the QTE simulations. We expect the proposed Bayesian rule to be useful for future cQED measurement and control experiments. (paper)

A Bayesian Panel Data Approach to Explaining Market Beta Dynamics

NARCIS (Netherlands)

R. Bauer (Rob); M.M.J.E. Cosemans (Mathijs); R. Frehen (Rik); P.C. Schotman (Peter)

2008-01-01

markdownabstractWe characterize the process that drives the market betas of individual stocks by setting up a hierarchical Bayesian panel data model that allows a flexible specification for beta. We show that combining the parametric relationship between betas and conditioning variables specified by

Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models.

Science.gov (United States)

Paz-Linares, Deirel; Vega-Hernández, Mayrim; Rojas-López, Pedro A; Valdés-Hernández, Pedro A; Martínez-Montes, Eduardo; Valdés-Sosa, Pedro A

2017-01-01

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods

A Bayesian decision approach to rainfall thresholds based flood warning

Directory of Open Access Journals (Sweden)

M. L. V. Martina

2006-01-01

Full Text Available Operational real time flood forecasting systems generally require a hydrological model to run in real time as well as a series of hydro-informatics tools to transform the flood forecast into relatively simple and clear messages to the decision makers involved in flood defense. The scope of this paper is to set forth the possibility of providing flood warnings at given river sections based on the direct comparison of the quantitative precipitation forecast with critical rainfall threshold values, without the need of an on-line real time forecasting system. This approach leads to an extremely simplified alert system to be used by non technical stakeholders and could also be used to supplement the traditional flood forecasting systems in case of system failures. The critical rainfall threshold values, incorporating the soil moisture initial conditions, result from statistical analyses using long hydrological time series combined with a Bayesian utility function minimization. In the paper, results of an application of the proposed methodology to the Sieve river, a tributary of the Arno river in Italy, are given to exemplify its practical applicability.

A Bayesian alternative for multi-objective ecohydrological model specification

Science.gov (United States)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

New sampling strategy using a Bayesian approach to assess iohexol clearance in kidney transplant recipients.

Science.gov (United States)

Benz-de Bretagne, I; Le Guellec, C; Halimi, J M; Gatault, P; Barbet, C; Alnajjar, A; Büchler, M; Lebranchu, Y; Andres, Christian Robert; Vourcʼh, P; Blasco, H

2012-06-01

%, and about 6% if the bounds of acceptance were set at ± 15%. This Bayesian approach can help to reduce the number of samples required to calculate GFR using Bröchner-Mortensen formula with good accuracy.

Bayesian image restoration, using configurations

DEFF Research Database (Denmark)

Thorarinsdottir, Thordis Linda

2006-01-01

configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for the salt and pepper noise. The inference in the model is discussed...

A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning

International Nuclear Information System (INIS)

Gudur, Madhu Sudhan Reddy; Hara, Wendy; Le, Quynh-Thu; Wang, Lei; Xing, Lei; Li, Ruijiang

2014-01-01

MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm’s accuracy of electron density mapping and its ability to detect bone in the head for eight patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as receiver operating characteristics for bone detection (HU > 200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p = 2  ×  10 −4 ), 283 for the intensity approach (p = 2  ×  10 −6 ) and 282

Bayesian analysis of rare events

Energy Technology Data Exchange (ETDEWEB)

Straub, Daniel, E-mail: straub@tum.de; 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.

Assessing offshore emergency evacuation behavior in a virtual environment using a Bayesian Network approach

International Nuclear Information System (INIS)

Musharraf, Mashrura; Smith, Jennifer; Khan, Faisal; Veitch, Brian; MacKinnon, Scott

2016-01-01

In the performance influencing factor (PIF) hierarchy, person-based influencing factors reside in the top level along with machine-based, team-based, organization-based and situation/stressor-based factors. Though person-based PIFs like morale, motivation, and attitude (MMA) play an important role in shaping performance, it is nearly impossible to assess such PIFs directly. However, it is possible to measure behavioral indicators (e.g. compliance, use of information) that can provide insight regarding the state of the unobservable person-based PIFs. One common approach to measuring these indicators is to carry out a self-reported questionnaire survey. Significant work has been done to make such questionnaires reliable, but the potential validity problem associated with any questionnaire is that the data are subjective and thus may bear a limited relationship to reality. This paper describes the use of a virtual environment to measure behavioral indicators, which in turn can be used as proxies to assess otherwise unobservable PIFs like MMA. A Bayesian Network (BN) model is first developed to define the relationship between person-based PIFs and measurable behavioral indicators. The paper then shows how these indicators can be measured using evidence collected from a virtual environment of an offshore petroleum installation. A study that focused on emergency evacuation scenarios was done with 36 participants. The participants were first assessed using a multiple choice test. They were then assessed based on their observed performance during simulated offshore emergency evacuation conditions. A comparison of the two assessments demonstrates the potential benefits and challenges of using virtual environments to assess behavioral indicators, and thus the person-based PIFs. - Highlights: • New approach to use virtual environment as measure of behavioral indicators. • New model to study morale, motivation, and attitude. • Bayesian Network model to define the

Bayesian Plackett-Luce Mixture Models for Partially Ranked Data.

Science.gov (United States)

Mollica, Cristina; Tardella, Luca

2017-06-01

The elicitation of an ordinal judgment on multiple alternatives is often required in many psychological and behavioral experiments to investigate preference/choice orientation of a specific population. The Plackett-Luce model is one of the most popular and frequently applied parametric distributions to analyze rankings of a finite set of items. The present work introduces a Bayesian finite mixture of Plackett-Luce models to account for unobserved sample heterogeneity of partially ranked data. We describe an efficient way to incorporate the latent group structure in the data augmentation approach and the derivation of existing maximum likelihood procedures as special instances of the proposed Bayesian method. Inference can be conducted with the combination of the Expectation-Maximization algorithm for maximum a posteriori estimation and the Gibbs sampling iterative procedure. We additionally investigate several Bayesian criteria for selecting the optimal mixture configuration and describe diagnostic tools for assessing the fitness of ranking distributions conditionally and unconditionally on the number of ranked items. The utility of the novel Bayesian parametric Plackett-Luce mixture for characterizing sample heterogeneity is illustrated with several applications to simulated and real preference ranked data. We compare our method with the frequentist approach and a Bayesian nonparametric mixture model both assuming the Plackett-Luce model as a mixture component. Our analysis on real datasets reveals the importance of an accurate diagnostic check for an appropriate in-depth understanding of the heterogenous nature of the partial ranking data.

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir

2013-11-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

Bayesian dynamic mediation analysis.

Science.gov (United States)

Huang, Jing; Yuan, Ying

2017-12-01

Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Detection of multiple damages employing best achievable eigenvectors under Bayesian inference

Science.gov (United States)

Prajapat, Kanta; Ray-Chaudhuri, Samit

2018-05-01

A novel approach is presented in this work to localize simultaneously multiple damaged elements in a structure along with the estimation of damage severity for each of the damaged elements. For detection of damaged elements, a best achievable eigenvector based formulation has been derived. To deal with noisy data, Bayesian inference is employed in the formulation wherein the likelihood of the Bayesian algorithm is formed on the basis of errors between the best achievable eigenvectors and the measured modes. In this approach, the most probable damage locations are evaluated under Bayesian inference by generating combinations of various possible damaged elements. Once damage locations are identified, damage severities are estimated using a Bayesian inference Markov chain Monte Carlo simulation. The efficiency of the proposed approach has been demonstrated by carrying out a numerical study involving a 12-story shear building. It has been found from this study that damage scenarios involving as low as 10% loss of stiffness in multiple elements are accurately determined (localized and severities quantified) even when 2% noise contaminated modal data are utilized. Further, this study introduces a term parameter impact (evaluated based on sensitivity of modal parameters towards structural parameters) to decide the suitability of selecting a particular mode, if some idea about the damaged elements are available. It has been demonstrated here that the accuracy and efficiency of the Bayesian quantification algorithm increases if damage localization is carried out a-priori. An experimental study involving a laboratory scale shear building and different stiffness modification scenarios shows that the proposed approach is efficient enough to localize the stories with stiffness modification.

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

KAUST Repository

Ait-El-Fquih, Boujemaa; El Gharamti, Mohamad; Hoteit, Ibrahim

2016-01-01

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

KAUST Repository

Ait-El-Fquih, Boujemaa

2016-08-12

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface ground-water models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model\\'s state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The joint EnKF directly updates the augmented state-parameter vector, whereas the dual EnKF empirically employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. To develop a Bayesian consistent dual approach and improve the state-parameter estimates and their consistency, we propose in this paper a one-step-ahead (OSA) smoothing formulation of the state-parameter Bayesian filtering problem from which we derive a new dual-type EnKF, the dual EnKF(OSA). Compared with the standard dual EnKF, it imposes a new update step to the state, which is shown to enhance the performance of the dual approach with almost no increase in the computational cost. Numerical experiments are conducted with a two-dimensional (2-D) synthetic groundwater aquifer model to investigate the performance and robustness of the proposed dual EnKFOSA, and to evaluate its results against those of the joint and dual EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, providing further reliable estimates of their uncertainties. Furthermore, it is found to be more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameter estimations than the joint and dual approaches.

BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS

Directory of Open Access Journals (Sweden)

Thordis Linda Thorarinsdottir

2011-05-01

Full Text Available In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.

Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

Science.gov (United States)

Knox, W. Bradley; Mengshoel, Ole

2009-01-01

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

A Bayesian approach to PET reconstruction using image-modeling Gibbs priors: Implementation and comparison

International Nuclear Information System (INIS)

Chan, M.T.; Herman, G.T.; Levitan, E.

1996-01-01

We demonstrate that (i) classical methods of image reconstruction from projections can be improved upon by considering the output of such a method as a distorted version of the original image and applying a Bayesian approach to estimate from it the original image (based on a model of distortion and on a Gibbs distribution as the prior) and (ii) by selecting an open-quotes image-modelingclose quotes prior distribution (i.e., one which is such that it is likely that a random sample from it shares important characteristics of the images of the application area) one can improve over another Gibbs prior formulated using only pairwise interactions. We illustrate our approach using simulated Positron Emission Tomography (PET) data from realistic brain phantoms. Since algorithm performance ultimately depends on the diagnostic task being performed. we examine a number of different medically relevant figures of merit to give a fair comparison. Based on a training-and-testing evaluation strategy, we demonstrate that statistically significant improvements can be obtained using the proposed approach

Stochastic models with heteroskedasticity: a Bayesian approach for Ibovespa returns - doi: 10.4025/actascitechnol.v35i2.13547

Directory of Open Access Journals (Sweden)

Sandra Cristina de Oliveira

2013-04-01

Full Text Available Current research compares the Bayesian estimates obtained for the parameters of processes of ARCH family with normal and Student’s t distributions for the conditional distribution of the return series. A non-informative prior distribution was adopted and a reparameterization of models under analysis was taken into account to map parameters’ space into real space. The procedure adopts a normal prior distribution for the transformed parameters. The posterior summaries were obtained by Monte Carlo Markov Chain (MCMC simulation methods. The methodology was evaluated by a series of Bovespa Index returns and the predictive ordinate criterion was employed to select the best adjustment model to the data. Results show that, as a rule, the proposed Bayesian approach provides satisfactory estimates and that the GARCH process with Student’s t distribution adjusted better to the data.  

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

Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach.

Science.gov (United States)

Zhang, J L; Li, Y P; Huang, G H; Baetz, B W; Liu, J

2017-06-01

In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic-possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

Directory of Open Access Journals (Sweden)

Sho Fukuda

2014-12-01

Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

Parameter Estimation of Structural Equation Modeling Using Bayesian Approach

Directory of Open Access Journals (Sweden)

Dewi Kurnia Sari

2016-05-01

Full Text Available Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization. In addition to the style of leadership, the success of an organization or company in achieving its objectives can also be influenced by the commitment of the organization. Where organizational commitment is a commitment created by each individual for the betterment of the organization. The purpose of this research is to obtain a model of leadership style and organizational commitment to job satisfaction and employee performance, and determine the factors that influence job satisfaction and employee performance using SEM with Bayesian approach. This research was conducted at Statistics FNI employees in Malang, with 15 people. The result of this study showed that the measurement model, all significant indicators measure each latent variable. Meanwhile in the structural model, it was concluded there are a significant difference between the variables of Leadership Style and Organizational Commitment toward Job Satisfaction directly as well as a significant difference between Job Satisfaction on Employee Performance. As for the influence of Leadership Style and variable Organizational Commitment on Employee Performance directly declared insignificant.

COBRA: a Bayesian approach to pulsar searching

Science.gov (United States)

Lentati, L.; Champion, D. J.; Kramer, M.; Barr, E.; Torne, P.

2018-02-01

We introduce COBRA, a GPU-accelerated Bayesian analysis package for performing pulsar searching, that uses candidates from traditional search techniques to set the prior used for the periodicity of the source, and performs a blind search in all remaining parameters. COBRA incorporates models for both isolated and accelerated systems, as well as both Keplerian and relativistic binaries, and exploits pulse phase information to combine search epochs coherently, over time, frequency or across multiple telescopes. We demonstrate the efficacy of our approach in a series of simulations that challenge typical search techniques, including highly aliased signals, and relativistic binary systems. In the most extreme case, we simulate an 8 h observation containing 24 orbits of a pulsar in a binary with a 30 M⊙ companion. Even in this scenario we show that we can build up from an initial low-significance candidate, to fully recovering the signal. We also apply the method to survey data of three pulsars from the globular cluster 47Tuc: PSRs J0024-7204D, J0023-7203J and J0024-7204R. This final pulsar is in a 1.6 h binary, the shortest of any pulsar in 47Tuc, and additionally shows significant scintillation. By allowing the amplitude of the source to vary as a function of time, however, we show that we are able to obtain optimal combinations of such noisy data. We also demonstrate the ability of COBRA to perform high-precision pulsar timing directly on the single pulse survey data, and obtain a 95 per cent upper limit on the eccentricity of PSR J0024-7204R of εb < 0.0007.

A systematic review of Bayesian articles in psychology : The last 25 years

OpenAIRE

van de Schoot, Rens; Winter, Sonja; Ryan, Oisín; Zondervan - Zwijnenburg, Mariëlle; Depaoli, Sarah

2017-01-01

Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statisti...

BUMPER: the Bayesian User-friendly Model for Palaeo-Environmental Reconstruction

Science.gov (United States)

Holden, Phil; Birks, John; Brooks, Steve; Bush, Mark; Hwang, Grace; Matthews-Bird, Frazer; Valencia, Bryan; van Woesik, Robert

2017-04-01

We describe the Bayesian User-friendly Model for Palaeo-Environmental Reconstruction (BUMPER), a Bayesian transfer function for inferring past climate and other environmental variables from microfossil assemblages. The principal motivation for a Bayesian approach is that the palaeoenvironment is treated probabilistically, and can be updated as additional data become available. Bayesian approaches therefore provide a reconstruction-specific quantification of the uncertainty in the data and in the model parameters. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast, requiring 2 seconds to build a 100-taxon model from a 100-site training-set on a standard personal computer. We apply the model's probabilistic framework to generate thousands of artificial training-sets under ideal assumptions. We then use these to demonstrate both the general applicability of the model and the sensitivity of reconstructions to the characteristics of the training-set, considering assemblage richness, taxon tolerances, and the number of training sites. We demonstrate general applicability to real data, considering three different organism types (chironomids, diatoms, pollen) and different reconstructed variables. In all of these applications an identically configured model is used, the only change being the input files that provide the training-set environment and taxon-count data.

Development of dynamic Bayesian models for web application test management

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Azarnova, T. V.; Polukhin, P. V.; Bondarenko, Yu V.; Kashirina, I. L.

2018-03-01

The mathematical apparatus of dynamic Bayesian networks is an effective and technically proven tool that can be used to model complex stochastic dynamic processes. According to the results of the research, mathematical models and methods of dynamic Bayesian networks provide a high coverage of stochastic tasks associated with error testing in multiuser software products operated in a dynamically changing environment. Formalized representation of the discrete test process as a dynamic Bayesian model allows us to organize the logical connection between individual test assets for multiple time slices. This approach gives an opportunity to present testing as a discrete process with set structural components responsible for the generation of test assets. Dynamic Bayesian network-based models allow us to combine in one management area individual units and testing components with different functionalities and a direct influence on each other in the process of comprehensive testing of various groups of computer bugs. The application of the proposed models provides an opportunity to use a consistent approach to formalize test principles and procedures, methods used to treat situational error signs, and methods used to produce analytical conclusions based on test results.

Multidimensional Poverty Indices and First Order Dominance Techniques: An Empirical Comparison of Different Approaches

DEFF Research Database (Denmark)

Hussain, M. Azhar; Permanyer, Iñaki

2018-01-01

techniques (FOD). Our empirical findings suggest that the FOD approach might be a reasonable cost-effective alternative to the United Nations Development Program (UNDP)’s flagship poverty indicator: the Multidimensional Poverty Index (MPI). To the extent that the FOD approach is able to uncover the socio...

Introduction of a methodology for visualization and graphical interpretation of Bayesian classification models.

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Balfer, Jenny; Bajorath, Jürgen

2014-09-22

Supervised machine learning models are widely used in chemoinformatics, especially for the prediction of new active compounds or targets of known actives. Bayesian classification methods are among the most popular machine learning approaches for the prediction of activity from chemical structure. Much work has focused on predicting structure-activity relationships (SARs) on the basis of experimental training data. By contrast, only a few efforts have thus far been made to rationalize the performance of Bayesian or other supervised machine learning models and better understand why they might succeed or fail. In this study, we introduce an intuitive approach for the visualization and graphical interpretation of naïve Bayesian classification models. Parameters derived during supervised learning are visualized and interactively analyzed to gain insights into model performance and identify features that determine predictions. The methodology is introduced in detail and applied to assess Bayesian modeling efforts and predictions on compound data sets of varying structural complexity. Different classification models and features determining their performance are characterized in detail. A prototypic implementation of the approach is provided.

Competing risk models in reliability systems, a Weibull distribution model with Bayesian analysis approach

International Nuclear Information System (INIS)

Iskandar, Ismed; Gondokaryono, Yudi Satria

2016-01-01

In reliability theory, the most important problem is to determine the reliability of a complex system from the reliability of its components. The weakness of most reliability theories is that the systems are described and explained as simply functioning or failed. In many real situations, the failures may be from many causes depending upon the age and the environment of the system and its components. Another problem in reliability theory is one of estimating the parameters of the assumed failure models. The estimation may be based on data collected over censored or uncensored life tests. In many reliability problems, the failure data are simply quantitatively inadequate, especially in engineering design and maintenance system. The Bayesian analyses are more beneficial than the classical one in such cases. The Bayesian estimation analyses allow us to combine past knowledge or experience in the form of an apriori distribution with life test data to make inferences of the parameter of interest. In this paper, we have investigated the application of the Bayesian estimation analyses to competing risk systems. The cases are limited to the models with independent causes of failure by using the Weibull distribution as our model. A simulation is conducted for this distribution with the objectives of verifying the models and the estimators and investigating the performance of the estimators for varying sample size. The simulation data are analyzed by using Bayesian and the maximum likelihood analyses. The simulation results show that the change of the true of parameter relatively to another will change the value of standard deviation in an opposite direction. For a perfect information on the prior distribution, the estimation methods of the Bayesian analyses are better than those of the maximum likelihood. The sensitivity analyses show some amount of sensitivity over the shifts of the prior locations. They also show the robustness of the Bayesian analysis within the range

Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department

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Wills Rachael A

2009-05-01

Full Text Available Abstract Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones, rather than objective reality. Bayesian analysis is (arguably a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

Bayesian nonparametric dictionary learning for compressed sensing MRI.

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Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

2014-12-01

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

Learning the Structure of Bayesian Network from Small Amount of Data

Directory of Open Access Journals (Sweden)

Bogdan COCU

2009-12-01

Full Text Available Many areas of artificial intelligence must handling with imperfection ofinformation. One of the ways to do this is using representation and reasoning withBayesian networks. Creation of a Bayesian network consists in two stages. First stage isto design the node structure and directed links between them. Choosing of a structurefor network can be done either through empirical developing by human experts orthrough machine learning algorithm. The second stage is completion of probabilitytables for each node. Using a machine learning method is useful, especially when wehave a big amount of leaning data. But in many fields the amount of data is small,incomplete and inconsistent. In this paper, we make a case study for choosing the bestlearning method for small amount of learning data. Means more experiments we dropconclusion of using existent methods for learning a network structure.

Bayesian Probability Theory

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von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo

2014-06-01

Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.

Improving the reliability of POD curves in NDI methods using a Bayesian inversion approach for uncertainty quantification

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Ben Abdessalem, A.; Jenson, F.; Calmon, P.

2016-02-01

This contribution provides an example of the possible advantages of adopting a Bayesian inversion approach to uncertainty quantification in nondestructive inspection methods. In such problem, the uncertainty associated to the random parameters is not always known and needs to be characterised from scattering signal measurements. The uncertainties may then correctly propagated in order to determine a reliable probability of detection curve. To this end, we establish a general Bayesian framework based on a non-parametric maximum likelihood function formulation and some priors from expert knowledge. However, the presented inverse problem is time-consuming and computationally intensive. To cope with this difficulty, we replace the real model by a surrogate one in order to speed-up the model evaluation and to make the problem to be computationally feasible for implementation. The least squares support vector regression is adopted as metamodelling technique due to its robustness to deal with non-linear problems. We illustrate the usefulness of this methodology through the control of tube with enclosed defect using ultrasonic inspection method.

Bayesian approach to the assessment of the population-specific risk of inhibitors in hemophilia A patients: a case study.

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Cheng, Ji; Iorio, Alfonso; Marcucci, Maura; Romanov, Vadim; Pullenayegum, Eleanor M; Marshall, John K; Thabane, Lehana

2016-01-01

Developing inhibitors is a rare event during the treatment of hemophilia A. The multifacets and uncertainty surrounding the development of inhibitors further complicate the process of estimating inhibitor rate from the limited data. Bayesian statistical modeling provides a useful tool in generating, enhancing, and exploring the evidence through incorporating all the available information. We built our Bayesian analysis using three study cases to estimate the inhibitor rates of patients with hemophilia A in three different scenarios: Case 1, a single cohort of previously treated patients (PTPs) or previously untreated patients; Case 2, a meta-analysis of PTP cohorts; and Case 3, a previously unexplored patient population - patients with baseline low-titer inhibitor or history of inhibitor development. The data used in this study were extracted from three published ADVATE (antihemophilic factor [recombinant] is a product of Baxter for treating hemophilia A) post-authorization surveillance studies. Noninformative and informative priors were applied to Bayesian standard (Case 1) or random-effects (Case 2 and Case 3) logistic models. Bayesian probabilities of satisfying three meaningful thresholds of the risk of developing a clinical significant inhibitor (10/100, 5/100 [high rates], and 1/86 [the Food and Drug Administration mandated cutoff rate in PTPs]) were calculated. The effect of discounting prior information or scaling up the study data was evaluated. Results based on noninformative priors were similar to the classical approach. Using priors from PTPs lowered the point estimate and narrowed the 95% credible intervals (Case 1: from 1.3 [0.5, 2.7] to 0.8 [0.5, 1.1]; Case 2: from 1.9 [0.6, 6.0] to 0.8 [0.5, 1.1]; Case 3: 2.3 [0.5, 6.8] to 0.7 [0.5, 1.1]). All probabilities of satisfying a threshold of 1/86 were above 0.65. Increasing the number of patients by two and ten times substantially narrowed the credible intervals for the single cohort study (1.4 [0.7, 2

A simple Bayesian approach to quantifying confidence level of adverse event incidence proportion in small samples.

Science.gov (United States)

Liu, Fang

2016-01-01

In both clinical development and post-marketing of a new therapy or a new treatment, incidence of an adverse event (AE) is always a concern. When sample sizes are small, large sample-based inferential approaches on an AE incidence proportion in a certain time period no longer apply. In this brief discussion, we introduce a simple Bayesian framework to quantify, in small sample studies and the rare AE case, (1) the confidence level that the incidence proportion of a particular AE p is over or below a threshold, (2) the lower or upper bounds on p with a certain level of confidence, and (3) the minimum required number of patients with an AE before we can be certain that p surpasses a specific threshold, or the maximum allowable number of patients with an AE after which we can no longer be certain that p is below a certain threshold, given a certain confidence level. The method is easy to understand and implement; the interpretation of the results is intuitive. This article also demonstrates the usefulness of simple Bayesian concepts when it comes to answering practical questions.

Probabilistic risk assessment framework for structural systems under multiple hazards using Bayesian statistics

Energy Technology Data Exchange (ETDEWEB)

Kwag, Shinyoung [North Carolina State University, Raleigh, NC 27695 (United States); Korea Atomic Energy Research Institute, Daejeon 305-353 (Korea, Republic of); Gupta, Abhinav, E-mail: agupta1@ncsu.edu [North Carolina State University, Raleigh, NC 27695 (United States)

2017-04-15

Highlights: • This study presents the development of Bayesian framework for probabilistic risk assessment (PRA) of structural systems under multiple hazards. • The concepts of Bayesian network and Bayesian inference are combined by mapping the traditionally used fault trees into a Bayesian network. • The proposed mapping allows for consideration of dependencies as well as correlations between events. • Incorporation of Bayesian inference permits a novel way for exploration of a scenario that is likely to result in a system level “vulnerability.” - Abstract: Conventional probabilistic risk assessment (PRA) methodologies (USNRC, 1983; IAEA, 1992; EPRI, 1994; Ellingwood, 2001) conduct risk assessment for different external hazards by considering each hazard separately and independent of each other. The risk metric for a specific hazard is evaluated by a convolution of the fragility and the hazard curves. The fragility curve for basic event is obtained by using empirical, experimental, and/or numerical simulation data for a particular hazard. Treating each hazard as an independently can be inappropriate in some cases as certain hazards are statistically correlated or dependent. Examples of such correlated events include but are not limited to flooding induced fire, seismically induced internal or external flooding, or even seismically induced fire. In the current practice, system level risk and consequence sequences are typically calculated using logic trees to express the causative relationship between events. In this paper, we present the results from a study on multi-hazard risk assessment that is conducted using a Bayesian network (BN) with Bayesian inference. The framework can consider statistical dependencies among risks from multiple hazards, allows updating by considering the newly available data/information at any level, and provide a novel way to explore alternative failure scenarios that may exist due to vulnerabilities.

Probabilistic risk assessment framework for structural systems under multiple hazards using Bayesian statistics

International Nuclear Information System (INIS)

Kwag, Shinyoung; Gupta, Abhinav

2017-01-01

Highlights: • This study presents the development of Bayesian framework for probabilistic risk assessment (PRA) of structural systems under multiple hazards. • The concepts of Bayesian network and Bayesian inference are combined by mapping the traditionally used fault trees into a Bayesian network. • The proposed mapping allows for consideration of dependencies as well as correlations between events. • Incorporation of Bayesian inference permits a novel way for exploration of a scenario that is likely to result in a system level “vulnerability.” - Abstract: Conventional probabilistic risk assessment (PRA) methodologies (USNRC, 1983; IAEA, 1992; EPRI, 1994; Ellingwood, 2001) conduct risk assessment for different external hazards by considering each hazard separately and independent of each other. The risk metric for a specific hazard is evaluated by a convolution of the fragility and the hazard curves. The fragility curve for basic event is obtained by using empirical, experimental, and/or numerical simulation data for a particular hazard. Treating each hazard as an independently can be inappropriate in some cases as certain hazards are statistically correlated or dependent. Examples of such correlated events include but are not limited to flooding induced fire, seismically induced internal or external flooding, or even seismically induced fire. In the current practice, system level risk and consequence sequences are typically calculated using logic trees to express the causative relationship between events. In this paper, we present the results from a study on multi-hazard risk assessment that is conducted using a Bayesian network (BN) with Bayesian inference. The framework can consider statistical dependencies among risks from multiple hazards, allows updating by considering the newly available data/information at any level, and provide a novel way to explore alternative failure scenarios that may exist due to vulnerabilities.

Bayesian component separation: The Planck experience

Science.gov (United States)

Wehus, Ingunn Kathrine; Eriksen, Hans Kristian

2018-05-01

Bayesian component separation techniques have played a central role in the data reduction process of Planck. The most important strength of this approach is its global nature, in which a parametric and physical model is fitted to the data. Such physical modeling allows the user to constrain very general data models, and jointly probe cosmological, astrophysical and instrumental parameters. This approach also supports statistically robust goodness-of-fit tests in terms of data-minus-model residual maps, which are essential for identifying residual systematic effects in the data. The main challenges are high code complexity and computational cost. Whether or not these costs are justified for a given experiment depends on its final uncertainty budget. We therefore predict that the importance of Bayesian component separation techniques is likely to increase with time for intensity mapping experiments, similar to what has happened in the CMB field, as observational techniques mature, and their overall sensitivity improves.

Predicting Football Matches Results using Bayesian Networks for English Premier League (EPL)

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Razali, Nazim; Mustapha, Aida; Yatim, Faiz Ahmad; Aziz, Ruhaya Ab

2017-08-01

The issues of modeling asscoiation football prediction model has become increasingly popular in the last few years and many different approaches of prediction models have been proposed with the point of evaluating the attributes that lead a football team to lose, draw or win the match. There are three types of approaches has been considered for predicting football matches results which include statistical approaches, machine learning approaches and Bayesian approaches. Lately, many studies regarding football prediction models has been produced using Bayesian approaches. This paper proposes a Bayesian Networks (BNs) to predict the results of football matches in term of home win (H), away win (A) and draw (D). The English Premier League (EPL) for three seasons of 2010-2011, 2011-2012 and 2012-2013 has been selected and reviewed. K-fold cross validation has been used for testing the accuracy of prediction model. The required information about the football data is sourced from a legitimate site at http://www.football-data.co.uk. BNs achieved predictive accuracy of 75.09% in average across three seasons. It is hoped that the results could be used as the benchmark output for future research in predicting football matches results.

Bayesian approach in the power electric systems study of reliability ...

African Journals Online (AJOL)

Subsequently, Bayesian methodologies are framed in an ampler problem list, based on the definition of an opportune "vector of state" and of a vector describing the system performances, aiming to the definition and the calculation or the estimation of system reliability. The purpose of our work is to establish a useful model ...

Multiscale Bayesian neural networks for soil water content estimation

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Jana, Raghavendra B.; Mohanty, Binayak P.; Springer, Everett P.

2008-08-01

Artificial neural networks (ANN) have been used for some time now to estimate soil hydraulic parameters from other available or more easily measurable soil properties. However, most such uses of ANNs as pedotransfer functions (PTFs) have been at matching spatial scales (1:1) of inputs and outputs. This approach assumes that the outputs are only required at the same scale as the input data. Unfortunately, this is rarely true. Different hydrologic, hydroclimatic, and contaminant transport models require soil hydraulic parameter data at different spatial scales, depending upon their grid sizes. While conventional (deterministic) ANNs have been traditionally used in these studies, the use of Bayesian training of ANNs is a more recent development. In this paper, we develop a Bayesian framework to derive soil water retention function including its uncertainty at the point or local scale using PTFs trained with coarser-scale Soil Survey Geographic (SSURGO)-based soil data. The approach includes an ANN trained with Bayesian techniques as a PTF tool with training and validation data collected across spatial extents (scales) in two different regions in the United States. The two study areas include the Las Cruces Trench site in the Rio Grande basin of New Mexico, and the Southern Great Plains 1997 (SGP97) hydrology experimental region in Oklahoma. Each region-specific Bayesian ANN is trained using soil texture and bulk density data from the SSURGO database (scale 1:24,000), and predictions of the soil water contents at different pressure heads with point scale data (1:1) inputs are made. The resulting outputs are corrected for bias using both linear and nonlinear correction techniques. The results show good agreement between the soil water content values measured at the point scale and those predicted by the Bayesian ANN-based PTFs for both the study sites. Overall, Bayesian ANNs coupled with nonlinear bias correction are found to be very suitable tools for deriving soil

Bayesian Geostatistical Modeling of Malaria Indicator Survey Data in Angola

Science.gov (United States)

Gosoniu, Laura; Veta, Andre Mia; Vounatsou, Penelope

2010-01-01

The 2006–2007 Angola Malaria Indicator Survey (AMIS) is the first nationally representative household survey in the country assessing coverage of the key malaria control interventions and measuring malaria-related burden among children under 5 years of age. In this paper, the Angolan MIS data were analyzed to produce the first smooth map of parasitaemia prevalence based on contemporary nationwide empirical data in the country. Bayesian geostatistical models were fitted to assess the effect of interventions after adjusting for environmental, climatic and socio-economic factors. Non-linear relationships between parasitaemia risk and environmental predictors were modeled by categorizing the covariates and by employing two non-parametric approaches, the B-splines and the P-splines. The results of the model validation showed that the categorical model was able to better capture the relationship between parasitaemia prevalence and the environmental factors. Model fit and prediction were handled within a Bayesian framework using Markov chain Monte Carlo (MCMC) simulations. Combining estimates of parasitaemia prevalence with the number of children under we obtained estimates of the number of infected children in the country. The population-adjusted prevalence ranges from in Namibe province to in Malanje province. The odds of parasitaemia in children living in a household with at least ITNs per person was by 41% lower (CI: 14%, 60%) than in those with fewer ITNs. The estimates of the number of parasitaemic children produced in this paper are important for planning and implementing malaria control interventions and for monitoring the impact of prevention and control activities. PMID:20351775

A Bayesian foundation for individual learning under uncertainty

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Christoph eMathys

2011-05-01

Full Text Available Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty. The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next higher level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i are analytical and extremely efficient, enabling real-time learning, (ii have a natural interpretation in terms of RL, and (iii contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty. These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability

A bayesian foundation for individual learning under uncertainty.

Science.gov (United States)

Mathys, Christoph; Daunizeau, Jean; Friston, Karl J; Stephan, Klaas E

2011-01-01

Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty). The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next highest level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean-field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i) are analytical and extremely efficient, enabling real-time learning, (ii) have a natural interpretation in terms of RL, and (iii) contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty). These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability theory.

Bayesian model selection of template forward models for EEG source reconstruction.

Science.gov (United States)

Strobbe, Gregor; van Mierlo, Pieter; De Vos, Maarten; Mijović, Bogdan; Hallez, Hans; Van Huffel, Sabine; López, José David; Vandenberghe, Stefaan

2014-06-01

Several EEG source reconstruction techniques have been proposed to identify the generating neuronal sources of electrical activity measured on the scalp. The solution of these techniques depends directly on the accuracy of the forward model that is inverted. Recently, a parametric empirical Bayesian (PEB) framework for distributed source reconstruction in EEG/MEG was introduced and implemented in the Statistical Parametric Mapping (SPM) software. The framework allows us to compare different forward modeling approaches, using real data, instead of using more traditional simulated data from an assumed true forward model. In the absence of a subject specific MR image, a 3-layered boundary element method (BEM) template head model is currently used including a scalp, skull and brain compartment. In this study, we introduced volumetric template head models based on the finite difference method (FDM). We constructed a FDM head model equivalent to the BEM model and an extended FDM model including CSF. These models were compared within the context of three different types of source priors related to the type of inversion used in the PEB framework: independent and identically distributed (IID) sources, equivalent to classical minimum norm approaches, coherence (COH) priors similar to methods such as LORETA, and multiple sparse priors (MSP). The resulting models were compared based on ERP data of 20 subjects using Bayesian model selection for group studies. The reconstructed activity was also compared with the findings of previous studies using functional magnetic resonance imaging. We found very strong evidence in favor of the extended FDM head model with CSF and assuming MSP. These results suggest that the use of realistic volumetric forward models can improve PEB EEG source reconstruction. Copyright © 2014 Elsevier Inc. All rights reserved.

Bayesian network as a modelling tool for risk management in agriculture

DEFF Research Database (Denmark)

Rasmussen, Svend; Madsen, Anders L.; Lund, Mogens

. In this paper we use Bayesian networks as an integrated modelling approach for representing uncertainty and analysing risk management in agriculture. It is shown how historical farm account data may be efficiently used to estimate conditional probabilities, which are the core elements in Bayesian network models....... We further show how the Bayesian network model RiBay is used for stochastic simulation of farm income, and we demonstrate how RiBay can be used to simulate risk management at the farm level. It is concluded that the key strength of a Bayesian network is the transparency of assumptions......, and that it has the ability to link uncertainty from different external sources to budget figures and to quantify risk at the farm level....

A Bayesian Approach for Structural Learning with Hidden Markov Models

Directory of Open Access Journals (Sweden)

Cen Li

2002-01-01

Full Text Available Hidden Markov Models(HMM have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.

MODELING INFORMATION SYSTEM AVAILABILITY BY USING BAYESIAN BELIEF NETWORK APPROACH

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Semir Ibrahimović

2016-03-01

Full Text Available Modern information systems are expected to be always-on by providing services to end-users, regardless of time and location. This is particularly important for organizations and industries where information systems support real-time operations and mission-critical applications that need to be available on 24  7  365 basis. Examples of such entities include process industries, telecommunications, healthcare, energy, banking, electronic commerce and a variety of cloud services. This article presents a modified Bayesian Belief Network model for predicting information system availability, introduced initially by Franke, U. and Johnson, P. (in article “Availability of enterprise IT systems – an expert based Bayesian model”. Software Quality Journal 20(2, 369-394, 2012 based on a thorough review of several dimensions of the information system availability, we proposed a modified set of determinants. The model is parameterized by using probability elicitation process with the participation of experts from the financial sector of Bosnia and Herzegovina. The model validation was performed using Monte Carlo simulation.

Bayesian error estimation in density-functional theory

DEFF Research Database (Denmark)

Mortensen, Jens Jørgen; Kaasbjerg, Kristen; Frederiksen, Søren Lund

2005-01-01

We present a practical scheme for performing error estimates for density-functional theory calculations. The approach, which is based on ideas from Bayesian statistics, involves creating an ensemble of exchange-correlation functionals by comparing with an experimental database of binding energies...

Intelligent condition monitoring of railway catenary systems : A Bayesian Network approach

NARCIS (Netherlands)

Wang, H.; Nunez Vicencio, Alfredo; Dollevoet, R.P.B.J.; Liu, Zhigang; Chen, Junwen; Spiryagin, Maksym; Gordon, Timothy; Cole, Colin; McSweeney, Tim

2017-01-01

This study proposes a Bayesian network (BN) dedicated for the intelligent condition monitoring of railway catenary systems. It combines five types of measurements related to catenary condition, namely the contact wire stagger, contact wire height, pantograph head displacement, pantograph head

Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate.

Science.gov (United States)

Dierssen, Heidi M

2010-10-05

Phytoplankton biomass and productivity have been continuously monitored from ocean color satellites for over a decade. Yet, the most widely used empirical approach for estimating chlorophyll a (Chl) from satellites can be in error by a factor of 5 or more. Such variability is due to differences in absorption and backscattering properties of phytoplankton and related concentrations of colored-dissolved organic matter (CDOM) and minerals. The empirical algorithms have built-in assumptions that follow the basic precept of biological oceanography--namely, oligotrophic regions with low phytoplankton biomass are populated with small phytoplankton, whereas more productive regions contain larger bloom-forming phytoplankton. With a changing world ocean, phytoplankton composition may shift in response to altered environmental forcing, and CDOM and mineral concentrations may become uncoupled from phytoplankton stocks, creating further uncertainty and error in the empirical approaches. Hence, caution is warranted when using empirically derived Chl to infer climate-related changes in ocean biology. The Southern Ocean is already experiencing climatic shifts and shows substantial errors in satellite-derived Chl for different phytoplankton assemblages. Accurate global assessments of phytoplankton will require improved technology and modeling, enhanced field observations, and ongoing validation of our "eyes in space."

Damage Detection in Railway Truss Bridges Employing Data Sensitivity under Bayesian Framework: A Numerical Investigation

Directory of Open Access Journals (Sweden)

Kanta Prajapat

2017-01-01

Full Text Available In general, for a structure it is quite difficult to get information about all of its modes through its dynamic response under ambient or external excitation. Therefore, it is vital to exhaustively use the available information in the acquired modal data to detect any damage in the structures. Further, in a Bayesian algorithm, it can be quite beneficial if a damage localization algorithm is first used to localize damage in the structure. In this way, the number of unknown parameters in the Bayesian algorithm can be reduced significantly and thus, the efficiency of Bayesian algorithm can be enhanced. This study exploits a mode shape and its derivative based approach to localize damage in truss type structures. For damage quantification purpose, a parameter sensitivity based prediction error variance approach in Bayesian model updating is employed, which allows extracting maximum information available in the modal data. This work employs the sensitivity based Bayesian algorithm to determine the posterior confidence in truss type railway bridges. Results of the study show that the proposed approach can efficiently detect and quantify damage in railway truss bridges.

Bayesian Mixed Hidden Markov Models: A Multi-Level Approach to Modeling Categorical Outcomes with Differential Misclassification

Science.gov (United States)

Zhang, Yue; Berhane, Kiros

2014-01-01

Questionnaire-based health status outcomes are often prone to misclassification. When studying the effect of risk factors on such outcomes, ignoring any potential misclassification may lead to biased effect estimates. Analytical challenges posed by these misclassified outcomes are further complicated when simultaneously exploring factors for both the misclassification and health processes in a multi-level setting. To address these challenges, we propose a fully Bayesian Mixed Hidden Markov Model (BMHMM) for handling differential misclassification in categorical outcomes in a multi-level setting. The BMHMM generalizes the traditional Hidden Markov Model (HMM) by introducing random effects into three sets of HMM parameters for joint estimation of the prevalence, transition and misclassification probabilities. This formulation not only allows joint estimation of all three sets of parameters, but also accounts for cluster level heterogeneity based on a multi-level model structure. Using this novel approach, both the true health status prevalence and the transition probabilities between the health states during follow-up are modeled as functions of covariates. The observed, possibly misclassified, health states are related to the true, but unobserved, health states and covariates. Results from simulation studies are presented to validate the estimation procedure, to show the computational efficiency due to the Bayesian approach and also to illustrate the gains from the proposed method compared to existing methods that ignore outcome misclassification and cluster level heterogeneity. We apply the proposed method to examine the risk factors for both asthma transition and misclassification in the Southern California Children's Health Study (CHS). PMID:24254432

A Bayesian perspective on age replacement with minimal repair

International Nuclear Information System (INIS)

Sheu, S.-H.; Yeh, R.H.; Lin, Y.-B.; Juang, M.-G.

1999-01-01

In this article, a Bayesian approach is developed for determining an optimal age replacement policy with minimal repair. By incorporating minimal repair, planned replacement, and unplanned replacement, the mathematical formulas of the expected cost per unit time are obtained for two cases - the infinite-horizon case and the one-replacement-cycle case. For each case, we show that there exists a unique and finite optimal age for replacement under some reasonable conditions. When the failure density is Weibull with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal age replacement policy. Further, various special cases are discussed in detail. Finally, a numerical example is given

Bayesian Averaging over Many Dynamic Model Structures with Evidence on the Great Ratios and Liquidity Trap Risk

NARCIS (Netherlands)

R.W. Strachan (Rodney); H.K. van Dijk (Herman)

2008-01-01

textabstractA Bayesian model averaging procedure is presented that makes use of a finite mixture of many model structures within the class of vector autoregressive (VAR) processes. It is applied to two empirical issues. First, stability of the Great Ratios in U.S. macro-economic time series is

Model Selection in Historical Research Using Approximate Bayesian Computation

Science.gov (United States)

Rubio-Campillo, Xavier

2016-01-01

Formal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to re-evaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. Case Study This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester’s laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Impact Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence. PMID:26730953

Using Bayesian statistics for modeling PTSD through Latent Growth Mixture Modeling: implementation and discussion

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Sarah Depaoli

2015-03-01

Full Text Available Background: After traumatic events, such as disaster, war trauma, and injuries including burns (which is the focus here, the risk to develop posttraumatic stress disorder (PTSD is approximately 10% (Breslau & Davis, 1992. Latent Growth Mixture Modeling can be used to classify individuals into distinct groups exhibiting different patterns of PTSD (Galatzer-Levy, 2015. Currently, empirical evidence points to four distinct trajectories of PTSD patterns in those who have experienced burn trauma. These trajectories are labeled as: resilient, recovery, chronic, and delayed onset trajectories (e.g., Bonanno, 2004; Bonanno, Brewin, Kaniasty, & Greca, 2010; Maercker, Gäbler, O'Neil, Schützwohl, & Müller, 2013; Pietrzak et al., 2013. The delayed onset trajectory affects only a small group of individuals, that is, about 4–5% (O'Donnell, Elliott, Lau, & Creamer, 2007. In addition to its low frequency, the later onset of this trajectory may contribute to the fact that these individuals can be easily overlooked by professionals. In this special symposium on Estimating PTSD trajectories (Van de Schoot, 2015a, we illustrate how to properly identify this small group of individuals through the Bayesian estimation framework using previous knowledge through priors (see, e.g., Depaoli & Boyajian, 2014; Van de Schoot, Broere, Perryck, Zondervan-Zwijnenburg, & Van Loey, 2015. Method: We used latent growth mixture modeling (LGMM (Van de Schoot, 2015b to estimate PTSD trajectories across 4 years that followed a traumatic burn. We demonstrate and compare results from traditional (maximum likelihood and Bayesian estimation using priors (see, Depaoli, 2012, 2013. Further, we discuss where priors come from and how to define them in the estimation process. Results: We demonstrate that only the Bayesian approach results in the desired theory-driven solution of PTSD trajectories. Since the priors are chosen subjectively, we also present a sensitivity analysis of the

Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?

Science.gov (United States)

Haker, Helene; Schneebeli, Maya; Stephan, Klaas Enno

2016-01-01

Diagnosis and individualized treatment of autism spectrum disorder (ASD) represent major problems for contemporary psychiatry. Tackling these problems requires guidance by a pathophysiological theory. In this paper, we consider recent theories that re-conceptualize ASD from a "Bayesian brain" perspective, which posit that the core abnormality of ASD resides in perceptual aberrations due to a disbalance in the precision of prediction errors (sensory noise) relative to the precision of predictions (prior beliefs). This results in percepts that are dominated by sensory inputs and less guided by top-down regularization and shifts the perceptual focus to detailed aspects of the environment with difficulties in extracting meaning. While these Bayesian theories have inspired ongoing empirical studies, their clinical implications have not yet been carved out. Here, we consider how this Bayesian perspective on disease mechanisms in ASD might contribute to improving clinical care for affected individuals. Specifically, we describe a computational strategy, based on generative (e.g., hierarchical Bayesian) models of behavioral and functional neuroimaging data, for establishing diagnostic tests. These tests could provide estimates of specific cognitive processes underlying ASD and delineate pathophysiological mechanisms with concrete treatment targets. Written with a clinical audience in mind, this article outlines how the development of computational diagnostics applicable to behavioral and functional neuroimaging data in routine clinical practice could not only fundamentally alter our concept of ASD but eventually also transform the clinical management of this disorder.

Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data

Science.gov (United States)

Varvia, Petri; Rautiainen, Miina; Seppänen, Aku

2018-03-01

In this paper, Bayesian inversion of a physically-based forest reflectance model is investigated to estimate of boreal forest canopy leaf area index (LAI) from EO-1 Hyperion hyperspectral data. The data consist of multiple forest stands with different species compositions and structures, imaged in three phases of the growing season. The Bayesian estimates of canopy LAI are compared to reference estimates based on a spectral vegetation index. The forest reflectance model contains also other unknown variables in addition to LAI, for example leaf single scattering albedo and understory reflectance. In the Bayesian approach, these variables are estimated simultaneously with LAI. The feasibility and seasonal variation of these estimates is also examined. Credible intervals for the estimates are also calculated and evaluated. The results show that the Bayesian inversion approach is significantly better than using a comparable spectral vegetation index regression.

Invited commentary: Lost in estimation--searching for alternatives to markov chains to fit complex Bayesian models.

Science.gov (United States)

Molitor, John

2012-03-01

Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.

β-empirical Bayes inference and model diagnosis of microarray data

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Hossain Mollah Mohammad

2012-06-01

Full Text Available Abstract Background Microarray data enables the high-throughput survey of mRNA expression profiles at the genomic level; however, the data presents a challenging statistical problem because of the large number of transcripts with small sample sizes that are obtained. To reduce the dimensionality, various Bayesian or empirical Bayes hierarchical models have been developed. However, because of the complexity of the microarray data, no model can explain the data fully. It is generally difficult to scrutinize the irregular patterns of expression that are not expected by the usual statistical gene by gene models. Results As an extension of empirical Bayes (EB procedures, we have developed the β-empirical Bayes (β-EB approach based on a β-likelihood measure which can be regarded as an ’evidence-based’ weighted (quasi- likelihood inference. The weight of a transcript t is described as a power function of its likelihood, fβ(yt|θ. Genes with low likelihoods have unexpected expression patterns and low weights. By assigning low weights to outliers, the inference becomes robust. The value of β, which controls the balance between the robustness and efficiency, is selected by maximizing the predictive β0-likelihood by cross-validation. The proposed β-EB approach identified six significant (p−5 contaminated transcripts as differentially expressed (DE in normal/tumor tissues from the head and neck of cancer patients. These six genes were all confirmed to be related to cancer; they were not identified as DE genes by the classical EB approach. When applied to the eQTL analysis of Arabidopsis thaliana, the proposed β-EB approach identified some potential master regulators that were missed by the EB approach. Conclusions The simulation data and real gene expression data showed that the proposed β-EB method was robust against outliers. The distribution of the weights was used to scrutinize the irregular patterns of expression and diagnose the model

Genetic Properties of Some Economic Traits in Isfahan Native Fowl Using Bayesian and REML Methods

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Salehinasab M

2015-12-01

Full Text Available The objective of the present study was to estimate heritability values for some performance and egg quality traits of native fowl in Isfahan breeding center using REML and Bayesian approaches. The records were about 51521 and 975 for performance and egg quality traits, respectively. At the first step, variance components were estimated for body weight at hatch (BW0, body weight at 8 weeks of age (BW8, weight at sexual maturity (WSM, egg yolk weight (YW, egg Haugh unit and eggshell thickness, via REML approach using ASREML software. At the second step, the same traits were analyzed via Bayesian approach using Gibbs3f90 software. In both approaches six different animal models were applied and the best model was determined using likelihood ratio test (LRT and deviance information criterion (DIC for REML and Bayesian approaches, respectively. Heritability estimates for BW0, WSM and ST were the same in both approaches. For BW0, LRT and DIC indexes confirmed that the model consisting maternal genetic, permanent environmental and direct genetic effects was significantly better than other models. For WSM, a model consisting of maternal permanent environmental effect in addition to direct genetic effect was the best. For shell thickness, the basic model consisting direct genetic effect was the best. The results for BW8, YW and Haugh unit, were different between the two approaches. The reason behind this tiny differences was that the convergence could not be achieved for some models in REML approach and thus for these traits the Bayesian approach estimated the variance components more accurately. The results indicated that ignoring maternal effects, overestimates the direct genetic variance and heritability for most of the traits. Also, the Bayesian-based software could take more variance components into account.

Bayesian Estimation of Small Effects in Exercise and Sports Science.

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Kerrie L Mengersen

Full Text Available The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL, and intermittent hypoxic exposure (IHE on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a 'magnitude-based inference' approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

Bayesian Estimation of Small Effects in Exercise and Sports Science.

Science.gov (United States)

Mengersen, Kerrie L; Drovandi, Christopher C; Robert, Christian P; Pyne, David B; Gore, Christopher J

2016-01-01

The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL), and intermittent hypoxic exposure (IHE)) on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a 'magnitude-based inference' approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

The application of bayesian statistic in data fit processing

International Nuclear Information System (INIS)

Guan Xingyin; Li Zhenfu; Song Zhaohui

2010-01-01

The rationality and disadvantage of least squares fitting that is usually used in data processing is analyzed, and the theory and commonly method that Bayesian statistic is applied in data processing is shown in detail. As it is proved in analysis, Bayesian approach avoid the limitative hypothesis that least squares fitting has in data processing, and the result has traits that it is more scientific and more easily understood, may replace the least squares fitting to apply in data processing. (authors)

Integrating distributed Bayesian inference and reinforcement learning for sensor management

NARCIS (Netherlands)

Grappiolo, C.; Whiteson, S.; Pavlin, G.; Bakker, B.

2009-01-01

This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically

Development of uncertainty-based work injury model using Bayesian structural equation modelling.

Science.gov (United States)

Chatterjee, Snehamoy

2014-01-01

This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.

MapReduce Based Parallel Bayesian Network for Manufacturing Quality Control

Science.gov (United States)

Zheng, Mao-Kuan; Ming, Xin-Guo; Zhang, Xian-Yu; Li, Guo-Ming

2017-09-01

Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the circumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian network of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly proportionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The integration of bigdata analytics and BN method offers a whole new perspective in manufacturing quality control.

Visualização dos padrões de variação da taxa de mortalidade infantil no Rio Grande do Sul, Brasil: comparação entre as abordagens Bayesiana Empírica e Totalmente Bayesiana Patterns of variation in the infant mortality rate in Rio Grande do Sul State, Brazil: comparison of empirical Bayesian and fully Bayesian approaches

Directory of Open Access Journals (Sweden)

Sabrina Letícia Couto da Silva

2011-07-01

Full Text Available A mortalidade infantil é um sensível indicador de saúde. Conhecer o seu perfil geográfico auxilia na formulação de estratégias de saúde pública. O mapeamento de doenças tem por objetivo descrever a distribuição geográfica das taxas de mortalidade ou incidência de doenças por intermédio de mapas. Em razão da alta instabilidade das taxas brutas quando há pequenas áreas, utilizam-se os métodos de suavização bayesiana, que se valem de informações de toda a região ou da vizinhança para estimar as taxas. O artigo faz a comparação entre os métodos Bayesiano Empírico e Totalmente Bayesiano para as taxas de mortalidade infantil (dados acumulados de 2001 a 2004 no Rio Grande do Sul, Brasil. O trabalho aponta as vantagens do uso dos estimadores bayesianos na visualização espacial dos mapas. Os métodos Bayesianos Empíricos apresentaram resultados muito semelhantes aos dos métodos Totalmente Bayesianos e possuem a grande vantagem de ser de fácil utilização por profissionais da área de saúde, destacando igualmente os principais padrões espaciais da taxa de mortalidade no Rio Grande do Sul no período estudado.Infant mortality is considered a sensitive health indicator, and knowledge of its geographical profile is essential for formulating appropriate public health policies. Disease mapping aims to describe the geographical distribution of disease incidence and mortality rates. Due to the heavy instability of crude rates in small areas, methods involving Bayesian smoothing of rates are used, drawing on information for the whole area or neighborhood to estimate the event rate. The current study compares empirical Bayesian (EB and fully Bayesian (FB methods for infant mortality rates (accumulated data from 2001 to 2004 in Rio Grande do Sul State, Brazil. This study highlights the advantages of Bayesian estimators for viewing and interpreting maps. For the problem at hand, EB and FB methods showed quite similar results and

Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks using Bayesian Model Averaging

NARCIS (Netherlands)

R.W. Strachan (Rodney); H.K. van Dijk (Herman)

2010-01-01

textabstractThe empirical support for a real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure. This procedure makes use of a finite mixture of many models within the class of vector autoregressive (VAR) processes. The linear VAR model is

Bayesian parameter estimation in probabilistic risk assessment

International Nuclear Information System (INIS)

Siu, Nathan O.; Kelly, Dana L.

1998-01-01

Bayesian statistical methods are widely used in probabilistic risk assessment (PRA) because of their ability to provide useful estimates of model parameters when data are sparse and because the subjective probability framework, from which these methods are derived, is a natural framework to address the decision problems motivating PRA. This paper presents a tutorial on Bayesian parameter estimation especially relevant to PRA. It summarizes the philosophy behind these methods, approaches for constructing likelihood functions and prior distributions, some simple but realistic examples, and a variety of cautions and lessons regarding practical applications. References are also provided for more in-depth coverage of various topics

Length Scales in Bayesian Automatic Adaptive Quadrature

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Adam Gh.

2016-01-01

Full Text Available Two conceptual developments in the Bayesian automatic adaptive quadrature approach to the numerical solution of one-dimensional Riemann integrals [Gh. Adam, S. Adam, Springer LNCS 7125, 1–16 (2012] are reported. First, it is shown that the numerical quadrature which avoids the overcomputing and minimizes the hidden floating point loss of precision asks for the consideration of three classes of integration domain lengths endowed with specific quadrature sums: microscopic (trapezoidal rule, mesoscopic (Simpson rule, and macroscopic (quadrature sums of high algebraic degrees of precision. Second, sensitive diagnostic tools for the Bayesian inference on macroscopic ranges, coming from the use of Clenshaw-Curtis quadrature, are derived.

Bayesian modeling using WinBUGS

CERN Document Server

Ntzoufras, Ioannis

2009-01-01

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...

Bayesian Optimization in High Dimensions via Random Embeddings

NARCIS (Netherlands)

Wang, Z.; Zoghi, M.; Hutter, F.; Matheson, D.; de Freitas, N.; Rossi, F.

2013-01-01

Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several

A Balanced Theoretical and Empirical Approach for the Development of a Design Support Tool

DEFF Research Database (Denmark)

Jensen, Thomas Aakjær; Hansen, Claus Thorp

1996-01-01

The introduction of a new design support system may change the engineering designer's work situation. Therefore, it may not be possible to derive all the functionalities for a design support system from solely empirical studies of manual design work. Alternatively the design support system could ...... system, indicating a proposal for how to balance a theoretical and empirical approach. The result of this research will be utilized in the development of a Designer's Workbench to support the synthesis activity in mechanical design....

Intention Recognition for Partial-Order Plans Using Dynamic Bayesian Networks

OpenAIRE

Krauthausen, Peter; Hanebeck, Uwe D.

2009-01-01

In this paper, a novel probabilistic approach to intention recognition for partial-order plans is proposed. The key idea is to exploit independences between subplans to substantially reduce the state space sizes in the compiled Dynamic Bayesian Networks. This makes inference more efficient. The main con- tributions are the computationally exploitable definition of subplan structures, the introduction of a novel Lay- ered Intention Model and a Dynamic Bayesian Net- work representation with an ...

When mechanism matters: Bayesian forecasting using models of ecological diffusion

Science.gov (United States)

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

A Bayesian approach to assess data from radionuclide activity analyses in environmental samples

International Nuclear Information System (INIS)

Barrera, Manuel; Lourdes Romero, M.; Nunez-Lagos, Rafael; Bernardo, Jose M.

2007-01-01

A Bayesian statistical approach is introduced to assess experimental data from the analyses of radionuclide activity concentration in environmental samples (low activities). A theoretical model has been developed that allows the use of known prior information about the value of the measurand (activity), together with the experimental value determined through the measurement. The model has been applied to data of the Inter-laboratory Proficiency Test organised periodically among Spanish environmental radioactivity laboratories that are producing the radiochemical results for the Spanish radioactive monitoring network. A global improvement of laboratories performance is produced when this prior information is taken into account. The prior information used in this methodology is an interval within which the activity is known to be contained, but it could be extended to any other experimental quantity with a different type of prior information available

A bayesian approach to inferring the genetic population structure of sugarcane accessions from INTA (Argentina

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Mariana Inés Pocovi

2015-06-01

Full Text Available Understanding the population structure and genetic diversity in sugarcane (Saccharum officinarum L. accessions from INTA germplasm bank (Argentina will be of great importance for germplasm collection and breeding improvement as it will identify diverse parental combinations to create segregating progenies with maximum genetic variability for further selection. A Bayesian approach, ordination methods (PCoA, Principal Coordinate Analysis and clustering analysis (UPGMA, Unweighted Pair Group Method with Arithmetic Mean were applied to this purpose. Sixty three INTA sugarcane hybrids were genotyped for 107 Simple Sequence Repeat (SSR and 136 Amplified Fragment Length Polymorphism (AFLP loci. Given the low probability values found with AFLP for individual assignment (4.7%, microsatellites seemed to perform better (54% for STRUCTURE analysis that revealed the germplasm to exist in five optimum groups with partly corresponding to their origin. However clusters shown high degree of admixture, F ST values confirmed the existence of differences among groups. Dissimilarity coefficients ranged from 0.079 to 0.651. PCoA separated sugarcane in groups that did not agree with those identified by STRUCTURE. The clustering including all genotypes neither showed resemblance to populations find by STRUCTURE, but clustering performed considering only individuals displaying a proportional membership > 0.6 in their primary population obtained with STRUCTURE showed close similarities. The Bayesian method indubitably brought more information on cultivar origins than classical PCoA and hierarchical clustering method.

Efficient design and inference in distributed Bayesian networks: an overview

NARCIS (Netherlands)

de Oude, P.; Groen, F.C.A.; Pavlin, G.; Bezhanishvili, N.; Löbner, S.; Schwabe, K.; Spada, L.

2011-01-01

This paper discusses an approach to distributed Bayesian modeling and inference, which is relevant for an important class of contemporary real world situation assessment applications. By explicitly considering the locality of causal relations, the presented approach (i) supports coherent distributed

Bayesian optimization for materials science

CERN Document Server

Packwood, Daniel

2017-01-01

This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science. Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While re...

Statistical inference an integrated Bayesianlikelihood approach

CERN Document Server

Aitkin, Murray

2010-01-01

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pre

An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Science.gov (United States)

Wayne S. Walker; Josef M. Kellndorfer; Elizabeth LaPoint; Michael Hoppus; James Westfall

2007-01-01

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the...

Using Bayesian networks to support decision-focused information retrieval

Energy Technology Data Exchange (ETDEWEB)

Lehner, P.; Elsaesser, C.; Seligman, L. [Mitre Corp., McLean, VA (United States)

1996-12-31

This paper has described an approach to controlling the process of pulling data/information from distributed data bases in a way that is specific to a persons specific decision making context. Our prototype implementation of this approach uses a knowledge-based planner to generate a plan, an automatically constructed Bayesian network to evaluate the plan, specialized processing of the network to derive key information items that would substantially impact the evaluation of the plan (e.g., determine that replanning is needed), automated construction of Standing Requests for Information (SRIs) which are automated functions that monitor changes and trends in distributed data base that are relevant to the key information items. This emphasis of this paper is on how Bayesian networks are used.

A Bayesian approach to landscape ecological risk assessment applied to the upper Grande Ronde watershed, Oregon

Science.gov (United States)

Kimberley K. Ayre; Wayne G. Landis

2012-01-01

We present a Bayesian network model based on the ecological risk assessment framework to evaluate potential impacts to habitats and resources resulting from wildfire, grazing, forest management activities, and insect outbreaks in a forested landscape in northeastern Oregon. The Bayesian network structure consisted of three tiers of nodes: landscape disturbances,...

An Empirical Study of Parameter Estimation for Stated Preference Experimental Design

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Fei Yang

2014-01-01

Full Text Available The stated preference experimental design can affect the reliability of the parameters estimation in discrete choice model. Some scholars have proposed some new experimental designs, such as D-efficient, Bayesian D-efficient. But insufficient empirical research has been conducted on the effectiveness of these new designs and there has been little comparative analysis of the new designs against the traditional designs. In this paper, a new metro connecting Chengdu and its satellite cities is taken as the research subject to demonstrate the validity of the D-efficient and Bayesian D-efficient design. Comparisons between these new designs and orthogonal design were made by the fit of model and standard deviation of parameters estimation; then the best model result is obtained to analyze the travel choice behavior. The results indicate that Bayesian D-efficient design works better than D-efficient design. Some of the variables can affect significantly the choice behavior of people, including the waiting time and arrival time. The D-efficient and Bayesian D-efficient design for MNL can acquire reliability result in ML model, but the ML model cannot develop the theory advantages of these two designs. Finally, the metro can handle over 40% passengers flow if the metro will be operated in the future.