Maximum margin Bayesian network classifiers.
Pernkopf, Franz; Wohlmayr, Michael; Tschiatschek, Sebastian
2012-03-01
We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.
Yu, Hwa-Lung; Wang, Chih-Hsih; Liu, Ming-Che; Kuo, Yi-Ming
2011-06-01
Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan) from 2005-2007.
Nagy, László G; Urban, Alexander; Orstadius, Leif; Papp, Tamás; Larsson, Ellen; Vágvölgyi, Csaba
2010-12-01
Recently developed comparative phylogenetic methods offer a wide spectrum of applications in evolutionary biology, although it is generally accepted that their statistical properties are incompletely known. Here, we examine and compare the statistical power of the ML and Bayesian methods with regard to selection of best-fit models of fruiting-body evolution and hypothesis testing of ancestral states on a real-life data set of a physiological trait (autodigestion) in the family Psathyrellaceae. Our phylogenies are based on the first multigene data set generated for the family. Two different coding regimes (binary and multistate) and two data sets differing in taxon sampling density are examined. The Bayesian method outperformed Maximum Likelihood with regard to statistical power in all analyses. This is particularly evident if the signal in the data is weak, i.e. in cases when the ML approach does not provide support to choose among competing hypotheses. Results based on binary and multistate coding differed only modestly, although it was evident that multistate analyses were less conclusive in all cases. It seems that increased taxon sampling density has favourable effects on inference of ancestral states, while model parameters are influenced to a smaller extent. The model best fitting our data implies that the rate of losses of deliquescence equals zero, although model selection in ML does not provide proper support to reject three of the four candidate models. The results also support the hypothesis that non-deliquescence (lack of autodigestion) has been ancestral in Psathyrellaceae, and that deliquescent fruiting bodies represent the preferred state, having evolved independently several times during evolution. Copyright © 2010 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Xiaokang Kou
2016-01-01
Full Text Available Land surface temperature (LST plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR remotely sensed data. Although passive microwaves (PMWs are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME method was introduced to merge 0.01° and 0.25° LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53 °C to 2.31 °C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies.
Applied Bayesian Hierarchical Methods
Congdon, Peter D
2010-01-01
Bayesian methods facilitate the analysis of complex models and data structures. Emphasizing data applications, alternative modeling specifications, and computer implementation, this book provides a practical overview of methods for Bayesian analysis of hierarchical models.
Bayesian and maximum likelihood estimation of genetic maps
DEFF Research Database (Denmark)
York, Thomas L.; Durrett, Richard T.; Tanksley, Steven;
2005-01-01
There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods...... that makes the Bayesian method applicable to large data sets. We present an extensive simulation study examining the statistical properties of the method and comparing it with the likelihood method implemented in Mapmaker. We show that the Maximum A Posteriori (MAP) estimator of the genetic distances...
Comparison Between Bayesian and Maximum Entropy Analyses of Flow Networks†
Directory of Open Access Journals (Sweden)
Steven H. Waldrip
2017-02-01
Full Text Available We compare the application of Bayesian inference and the maximum entropy (MaxEnt method for the analysis of ﬂow networks, such as water, electrical and transport networks. The two methods have the advantage of allowing a probabilistic prediction of ﬂow rates and other variables, when there is insufﬁcient information to obtain a deterministic solution, and also allow the effects of uncertainty to be included. Both methods of inference update a prior to a posterior probability density function (pdf by the inclusion of new information, in the form of data or constraints. The MaxEnt method maximises an entropy function subject to constraints, using the method of Lagrange multipliers,to give the posterior, while the Bayesian method ﬁnds its posterior by multiplying the prior with likelihood functions incorporating the measured data. In this study, we examine MaxEnt using soft constraints, either included in the prior or as probabilistic constraints, in addition to standard moment constraints. We show that when the prior is Gaussian,both Bayesian inference and the MaxEnt method with soft prior constraints give the same posterior means, but their covariances are different. In the Bayesian method, the interactions between variables are applied through the likelihood function, using second or higher-order cross-terms within the posterior pdf. In contrast, the MaxEnt method incorporates interactions between variables using Lagrange multipliers, avoiding second-order correlation terms in the posterior covariance. The MaxEnt method with soft prior constraints, therefore, has a numerical advantage over Bayesian inference, in that the covariance terms are avoided in its integrations. The second MaxEnt method with soft probabilistic constraints is shown to give posterior means of similar, but not identical, structure to the other two methods, due to its different formulation.
Bayesian Methods and Universal Darwinism
Campbell, John
2010-01-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a 'copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that system...
Proscriptive Bayesian Programming and Maximum Entropy: a Preliminary Study
Koike, Carla Cavalcante
2008-11-01
Some problems found in robotics systems, as avoiding obstacles, can be better described using proscriptive commands, where only prohibited actions are indicated in contrast to prescriptive situations, which demands that a specific command be specified. An interesting question arises regarding the possibility to learn automatically if proscriptive commands are suitable and which parametric function could be better applied. Lately, a great variety of problems in robotics domain are object of researches using probabilistic methods, including the use of Maximum Entropy in automatic learning for robot control systems. This works presents a preliminary study on automatic learning of proscriptive robot control using maximum entropy and using Bayesian Programming. It is verified whether Maximum entropy and related methods can favour proscriptive commands in an obstacle avoidance task executed by a mobile robot.
Bayesian Methods and Universal Darwinism
Campbell, John
2009-12-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent Champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a `copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the Operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that Systems will evolve to states of highest entropy subject to the constraints of scientific law. This principle may be inverted to provide illumination as to the nature of scientific law. Our best cosmological theories suggest the universe contained much less complexity during the period shortly after the Big Bang than it does at present. The scientific subject matter of atomic physics, chemistry, biology and the social sciences has been created since that time. An explanation is proposed for the existence of this subject matter as due to the evolution of constraints in the form of adaptations imposed on Maximum Entropy. It is argued these adaptations were discovered and instantiated through the Operations of a succession of Darwinian processes.
Bayesian Methods for Statistical Analysis
Puza, Borek
2015-01-01
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete c...
Bayesian methods for hackers probabilistic programming and Bayesian inference
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...
Bayesian adaptive methods for clinical trials
Berry, Scott M; Muller, Peter
2010-01-01
Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISP...
Deep Learning and Bayesian Methods
Prosper, Harrison B.
2017-03-01
A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Deep Learning and Bayesian Methods
Directory of Open Access Journals (Sweden)
Prosper Harrison B.
2017-01-01
Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Institute of Scientific and Technical Information of China (English)
GaoChunwen; XuJingzhen; RichardSinding-Larsen
2005-01-01
A Bayesian approach using Markov chain Monte Carlo algorithms has been developed to analyze Smith's discretized version of the discovery process model. It avoids the problems involved in the maximum likelihood method by effectively making use of the information from the prior distribution and that from the discovery sequence according to posterior probabilities. All statistical inferences about the parameters of the model and total resources can be quantified by drawing samples directly from the joint posterior distribution. In addition, statistical errors of the samples can be easily assessed and the convergence properties can be monitored during the sampling. Because the information contained in a discovery sequence is not enough to estimate all parameters, especially the number of fields, geologically justified prior information is crucial to the estimation. The Bayesian approach allows the analyst to specify his subjective estimates of the required parameters and his degree of uncertainty about the estimates in a clearly identified fashion throughout the analysis. As an example, this approach is applied to the same data of the North Sea on which Smith demonstrated his maximum likelihood method. For this case, the Bayesian approach has really improved the overly pessimistic results and downward bias of the maximum likelihood procedure.
Bayesian methods for measures of agreement
Broemeling, Lyle D
2009-01-01
Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software.Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation...
Variational bayesian method of estimating variance components.
Arakawa, Aisaku; Taniguchi, Masaaki; Hayashi, Takeshi; Mikawa, Satoshi
2016-07-01
We developed a Bayesian analysis approach by using a variational inference method, a so-called variational Bayesian method, to determine the posterior distributions of variance components. This variational Bayesian method and an alternative Bayesian method using Gibbs sampling were compared in estimating genetic and residual variance components from both simulated data and publically available real pig data. In the simulated data set, we observed strong bias toward overestimation of genetic variance for the variational Bayesian method in the case of low heritability and low population size, and less bias was detected with larger population sizes in both methods examined. The differences in the estimates of variance components between the variational Bayesian and the Gibbs sampling were not found in the real pig data. However, the posterior distributions of the variance components obtained with the variational Bayesian method had shorter tails than those obtained with the Gibbs sampling. Consequently, the posterior standard deviations of the genetic and residual variances of the variational Bayesian method were lower than those of the method using Gibbs sampling. The computing time required was much shorter with the variational Bayesian method than with the method using Gibbs sampling.
SYNTHESIZED EXPECTED BAYESIAN METHOD OF PARAMETRIC ESTIMATE
Institute of Scientific and Technical Information of China (English)
Ming HAN; Yuanyao DING
2004-01-01
This paper develops a new method of parametric estimate, which is named as "synthesized expected Bayesian method". When samples of products are tested and no failure events occur, thedefinition of expected Bayesian estimate is introduced and the estimates of failure probability and failure rate are provided. After some failure information is introduced by making an extra-test, a synthesized expected Bayesian method is defined and used to estimate failure probability, failure rateand some other parameters in exponential distribution and Weibull distribution of populations. Finally,calculations are performed according to practical problems, which show that the synthesized expected Bayesian method is feasible and easy to operate.
A Modified Extended Bayesian Method for Parameter Estimation
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper presents a modified extended Bayesian method for parameter estimation. In this method the mean value of the a priori estimation is taken from the values of the estimated parameters in the previous iteration step. In this way, the parameter covariance matrix can be automatically updated during the estimation procedure, thereby avoiding the selection of an empirical parameter. Because the extended Bayesian method can be regarded as a Tikhonov regularization, this new method is more stable than both the least-squares method and the maximum likelihood method. The validity of the proposed method is illustrated by two examples: one based on simulated data and one based on real engineering data.
Bayesian flood forecasting methods: A review
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
Estimating Tree Height-Diameter Models with the Bayesian Method
Directory of Open Access Journals (Sweden)
Xiongqing Zhang
2014-01-01
Full Text Available Six candidate height-diameter models were used to analyze the height-diameter relationships. The common methods for estimating the height-diameter models have taken the classical (frequentist approach based on the frequency interpretation of probability, for example, the nonlinear least squares method (NLS and the maximum likelihood method (ML. The Bayesian method has an exclusive advantage compared with classical method that the parameters to be estimated are regarded as random variables. In this study, the classical and Bayesian methods were used to estimate six height-diameter models, respectively. Both the classical method and Bayesian method showed that the Weibull model was the “best” model using data1. In addition, based on the Weibull model, data2 was used for comparing Bayesian method with informative priors with uninformative priors and classical method. The results showed that the improvement in prediction accuracy with Bayesian method led to narrower confidence bands of predicted value in comparison to that for the classical method, and the credible bands of parameters with informative priors were also narrower than uninformative priors and classical method. The estimated posterior distributions for parameters can be set as new priors in estimating the parameters using data2.
Directory of Open Access Journals (Sweden)
Haseeb A. Khan
2008-01-01
Full Text Available This investigation was aimed to compare the inference of antelope phylogenies resulting from the 16S rRNA, cytochrome-b (cyt-b and d-loop segments of mitochondrial DNA using three different computational models including Bayesian (BA, maximum parsimony (MP and unweighted pair group method with arithmetic mean (UPGMA. The respective nucleotide sequences of three Oryx species (Oryx leucoryx, Oryx dammah and Oryx gazella and an out-group (Addax nasomaculatus were aligned and subjected to BA, MP and UPGMA models for comparing the topologies of respective phylogenetic trees. The 16S rRNA region possessed the highest frequency of conserved sequences (97.65% followed by cyt-b (94.22% and d-loop (87.29%. There were few transitions (2.35% and none transversions in 16S rRNA as compared to cyt-b (5.61% transitions and 0.17% transversions and d-loop (11.57% transitions and 1.14% transversions while com- paring the four taxa. All the three mitochondrial segments clearly differentiated the genus Addax from Oryx using the BA or UPGMA models. The topologies of all the gamma-corrected Bayesian trees were identical irrespective of the marker type. The UPGMA trees resulting from 16S rRNA and d-loop sequences were also identical (Oryx dammah grouped with Oryx leucoryx to Bayesian trees except that the UPGMA tree based on cyt-b showed a slightly different phylogeny (Oryx dammah grouped with Oryx gazella with a low bootstrap support. However, the MP model failed to differentiate the genus Addax from Oryx. These findings demonstrate the efficiency and robustness of BA and UPGMA methods for phylogenetic analysis of antelopes using mitochondrial markers.
Variational Bayesian Approximation methods for inverse problems
Mohammad-Djafari, Ali
2012-09-01
Variational Bayesian Approximation (VBA) methods are recent tools for effective Bayesian computations. In this paper, these tools are used for inverse problems where the prior models include hidden variables and where where the estimation of the hyper parameters has also to be addressed. In particular two specific prior models (Student-t and mixture of Gaussian models) are considered and details of the algorithms are given.
Brus, D.J.; Bogaert, P.; Heuvelink, G.B.M.
2008-01-01
Bayesian Maximum Entropy was used to estimate the probabilities of occurrence of soil categories in the Netherlands, and to simulate realizations from the associated multi-point pdf. Besides the hard observations (H) of the categories at 8369 locations, the soil map of the Netherlands 1:50 000 was u
Bayesian Methods for Radiation Detection and Dosimetry
Groer, Peter G
2002-01-01
We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed comp...
New multifactor spatial prediction method based on Bayesian maximum entropy%基于贝叶斯最大熵的多因子空间属性预测新方法
Institute of Scientific and Technical Information of China (English)
杨勇; 张楚天; 贺立源
2013-01-01
Summary The spatial distributions of soil properties (e.g.,organic matter and heavy metal content) are vital to soil quality evaluation and regional environment assessment.Currently,the spatial distribution of soil properties is usually predicted with classical geostatistics or environmental correlation.These two methods are different in theory.Geostatistics is based on spatial correlation of sampling points.However,it contains some deficiencies, such as the lack of effective utilization of environmental information,the smoothing effect of predicted results, difficult to meet the assumption of single point to multipoint Gaussian distribution etc .On the other hand,the theoretical basis of environmental correlation is based on the relationship between soil and environment,but it ignores the spatial correlation among sampling points.These two methods complement each other.Thus,it is very important to study how to integrate these two methods,so that the spatial correlation among sampling points and the relationship between soil and environmental factors can both be used to improve the prediction accuracy. We propose a new spatial prediction method based on the theory of Bayesian maximum entropy (BME), which is one of the most well-known modern spatiotemporal geostatistical techniques.The main objective is to incorporate the results of classical geostatistics and quantitative soil-landscape model in the BME framework. The result of ordinary Kriging was taken as the priori probability density function (pdf),as well as the sampling data as hard data,and the results of environmental correlation as soft data.Posterior pdf is calculated with priori pdf,hard data and soft data.According to the posterior pdf,the predicted values of non-sampling points could be obtained,which not only contained the spatial correlation between sample points,but also took into account the relationship between soil properties and the environment.Meanwhile,the soil organic matter contents in
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...
Boedeker, Peter
2017-01-01
Hierarchical linear modeling (HLM) is a useful tool when analyzing data collected from groups. There are many decisions to be made when constructing and estimating a model in HLM including which estimation technique to use. Three of the estimation techniques available when analyzing data with HLM are maximum likelihood, restricted maximum…
Hessian PDF reweighting meets the Bayesian methods
Paukkunen, Hannu
2014-01-01
We discuss the Hessian PDF reweighting - a technique intended to estimate the effects that new measurements have on a set of PDFs. The method stems straightforwardly from considering new data in a usual $\\chi^2$-fit and it naturally incorporates also non-zero values for the tolerance, $\\Delta\\chi^2>1$. In comparison to the contemporary Bayesian reweighting techniques, there is no need to generate large ensembles of PDF Monte-Carlo replicas, and the observables need to be evaluated only with the central and the error sets of the original PDFs. In spite of the apparently rather different methodologies, we find that the Hessian and the Bayesian techniques are actually equivalent if the $\\Delta\\chi^2$ criterion is properly included to the Bayesian likelihood function that is a simple exponential.
Directory of Open Access Journals (Sweden)
Che Wan Jasimah bt Wan Mohamed Radzi
2016-11-01
Full Text Available Several factors may influence children’s lifestyle. The main purpose of this study is to introduce a children’s lifestyle index framework and model it based on structural equation modeling (SEM with Maximum likelihood (ML and Bayesian predictors. This framework includes parental socioeconomic status, household food security, parental lifestyle, and children’s lifestyle. The sample for this study involves 452 volunteer Chinese families with children 7–12 years old. The experimental results are compared in terms of root mean square error, coefficient of determination, mean absolute error, and mean absolute percentage error metrics. An analysis of the proposed causal model suggests there are multiple significant interconnections among the variables of interest. According to both Bayesian and ML techniques, the proposed framework illustrates that parental socioeconomic status and parental lifestyle strongly impact children’s lifestyle. The impact of household food security on children’s lifestyle is rejected. However, there is a strong relationship between household food security and both parental socioeconomic status and parental lifestyle. Moreover, the outputs illustrate that the Bayesian prediction model has a good fit with the data, unlike the ML approach. The reasons for this discrepancy between ML and Bayesian prediction are debated and potential advantages and caveats with the application of the Bayesian approach in future studies are discussed.
Che Wan Jasimah bt Wan Mohamed Radzi; Huang Hui; Hashem Salarzadeh Jenatabadi
2016-01-01
Several factors may influence children’s lifestyle. The main purpose of this study is to introduce a children’s lifestyle index framework and model it based on structural equation modeling (SEM) with Maximum likelihood (ML) and Bayesian predictors. This framework includes parental socioeconomic status, household food security, parental lifestyle, and children’s lifestyle. The sample for this study involves 452 volunteer Chinese families with children 7–12 years old. The experimental results a...
BONNSAI: correlated stellar observables in Bayesian methods
Schneider, F R N; Fossati, L; Langer, N; de Koter, A
2016-01-01
In an era of large spectroscopic surveys of stars and big data, sophisticated statistical methods become more and more important in order to infer fundamental stellar parameters such as mass and age. Bayesian techniques are powerful methods because they can match all available observables simultaneously to stellar models while taking prior knowledge properly into account. However, in most cases it is assumed that observables are uncorrelated which is generally not the case. Here, we include correlations in the Bayesian code BONNSAI by incorporating the covariance matrix in the likelihood function. We derive a parametrisation of the covariance matrix that, in addition to classical uncertainties, only requires the specification of a correlation parameter that describes how observables co-vary. Our correlation parameter depends purely on the method with which observables have been determined and can be analytically derived in some cases. This approach therefore has the advantage that correlations can be accounte...
Yu, Hwa-Lung; Wang, Chih-Hsin
2013-02-05
Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.
Jat, Prahlad; Serre, Marc L
2016-12-01
Widespread contamination of surface water chloride is an emerging environmental concern. Consequently accurate and cost-effective methods are needed to estimate chloride along all river miles of potentially contaminated watersheds. Here we introduce a Bayesian Maximum Entropy (BME) space/time geostatistical estimation framework that uses river distances, and we compare it with Euclidean BME to estimate surface water chloride from 2005 to 2014 in the Gunpowder-Patapsco, Severn, and Patuxent subbasins in Maryland. River BME improves the cross-validation R(2) by 23.67% over Euclidean BME, and river BME maps are significantly different than Euclidean BME maps, indicating that it is important to use river BME maps to assess water quality impairment. The river BME maps of chloride concentration show wide contamination throughout Baltimore and Columbia-Ellicott cities, the disappearance of a clean buffer separating these two large urban areas, and the emergence of multiple localized pockets of contamination in surrounding areas. The number of impaired river miles increased by 0.55% per year in 2005-2009 and by 1.23% per year in 2011-2014, corresponding to a marked acceleration of the rate of impairment. Our results support the need for control measures and increased monitoring of unassessed river miles.
Bayesian methods of astronomical source extraction
Oliver, R S S S
2005-01-01
We present two new source extraction methods, based on the Bayesian statistical formalism. The first is a source detection filter, able to simultaneously detect point sources and estimate the image background. The second is an advanced photometry technique, which measures the flux, position (to sub-pixel accuracy), local background and point spread function of a previously-detected source. In both cases, we use the Bayesian Information Criterion (BIC) to compare the relative likelihood of different models. We apply the source detection filter to simulated Herschel-SPIRE data and show the filter's ability to both detect point sources and also simultaneously estimate the image background. We use the photometry method to analyse a simple simulated image containing a source of unknown flux, position and point spread function; we not only accurately measure these parameters, but also determine their uncertainties (using Markov-Chain Monte Carlo sampling). We also characterise the nature of the source (for example,...
Prior approval: the growth of Bayesian methods in psychology.
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.
Unification of Field Theory and Maximum Entropy Methods for Learning Probability Densities
Kinney, Justin B
2014-01-01
Bayesian field theory and maximum entropy are two methods for learning smooth probability distributions (a.k.a. probability densities) from finite sampled data. Both methods were inspired by statistical physics, but the relationship between them has remained unclear. Here I show that Bayesian field theory subsumes maximum entropy density estimation. In particular, the most common maximum entropy methods are shown to be limiting cases of Bayesian inference using field theory priors that impose no boundary conditions on candidate densities. This unification provides a natural way to test the validity of the maximum entropy assumption on one's data. It also provides a better-fitting nonparametric density estimate when the maximum entropy assumption is rejected.
A Bayesian method for pulsar template generation
Imgrund, M; Kramer, M; Lesch, H
2015-01-01
Extracting Times of Arrival from pulsar radio signals depends on the knowledge of the pulsars pulse profile and how this template is generated. We examine pulsar template generation with Bayesian methods. We will contrast the classical generation mechanism of averaging intensity profiles with a new approach based on Bayesian inference. We introduce the Bayesian measurement model imposed and derive the algorithm to reconstruct a "statistical template" out of noisy data. The properties of these "statistical templates" are analysed with simulated and real measurement data from PSR B1133+16. We explain how to put this new form of template to use in analysing secondary parameters of interest and give various examples: We implement a nonlinear filter for determining ToAs of pulsars. Applying this method to data from PSR J1713+0747 we derive ToAs self consistently, meaning all epochs were timed and we used the same epochs for template generation. While the average template contains fluctuations and noise as unavoida...
Maximum-likelihood method in quantum estimation
Paris, M G A; Sacchi, M F
2001-01-01
The maximum-likelihood method for quantum estimation is reviewed and applied to the reconstruction of density matrix of spin and radiation as well as to the determination of several parameters of interest in quantum optics.
Bayesian non- and semi-parametric methods and applications
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
Numerical Methods for Bayesian Inverse Problems
Ernst, Oliver
2014-01-06
We present recent results on Bayesian inversion for a groundwater flow problem with an uncertain conductivity field. In particular, we show how direct and indirect measurements can be used to obtain a stochastic model for the unknown. The main tool here is Bayes’ theorem which merges the indirect data with the stochastic prior model for the conductivity field obtained by the direct measurements. Further, we demonstrate how the resulting posterior distribution of the quantity of interest, in this case travel times of radionuclide contaminants, can be obtained by Markov Chain Monte Carlo (MCMC) simulations. Moreover, we investigate new, promising MCMC methods which exploit geometrical features of the posterior and which are suited to infinite dimensions.
Bayesian Network Enhanced with Structural Reliability Methods: Methodology
Straub, Daniel; Der Kiureghian, Armen
2012-01-01
We combine Bayesian networks (BNs) and structural reliability methods (SRMs) to create a new computational framework, termed enhanced Bayesian network (eBN), for reliability and risk analysis of engineering structures and infrastructure. BNs are efficient in representing and evaluating complex probabilistic dependence structures, as present in infrastructure and structural systems, and they facilitate Bayesian updating of the model when new information becomes available. On the other hand, SR...
Assessment of substitution model adequacy using frequentist and Bayesian methods.
Ripplinger, Jennifer; Sullivan, Jack
2010-12-01
In order to have confidence in model-based phylogenetic methods, such as maximum likelihood (ML) and Bayesian analyses, one must use an appropriate model of molecular evolution identified using statistically rigorous criteria. Although model selection methods such as the likelihood ratio test and Akaike information criterion are widely used in the phylogenetic literature, model selection methods lack the ability to reject all models if they provide an inadequate fit to the data. There are two methods, however, that assess absolute model adequacy, the frequentist Goldman-Cox (GC) test and Bayesian posterior predictive simulations (PPSs), which are commonly used in conjunction with the multinomial log likelihood test statistic. In this study, we use empirical and simulated data to evaluate the adequacy of common substitution models using both frequentist and Bayesian methods and compare the results with those obtained with model selection methods. In addition, we investigate the relationship between model adequacy and performance in ML and Bayesian analyses in terms of topology, branch lengths, and bipartition support. We show that tests of model adequacy based on the multinomial likelihood often fail to reject simple substitution models, especially when the models incorporate among-site rate variation (ASRV), and normally fail to reject less complex models than those chosen by model selection methods. In addition, we find that PPSs often fail to reject simpler models than the GC test. Use of the simplest substitution models not rejected based on fit normally results in similar but divergent estimates of tree topology and branch lengths. In addition, use of the simplest adequate substitution models can affect estimates of bipartition support, although these differences are often small with the largest differences confined to poorly supported nodes. We also find that alternative assumptions about ASRV can affect tree topology, tree length, and bipartition support. Our
Xu, Yadong; Serre, Marc L; Reyes, Jeanette; Vizuete, William
2016-04-19
To improve ozone exposure estimates for ambient concentrations at a national scale, we introduce our novel Regionalized Air Quality Model Performance (RAMP) approach to integrate chemical transport model (CTM) predictions with the available ozone observations using the Bayesian Maximum Entropy (BME) framework. The framework models the nonlinear and nonhomoscedastic relation between air pollution observations and CTM predictions and for the first time accounts for variability in CTM model performance. A validation analysis using only noncollocated data outside of a validation radius rv was performed and the R(2) between observations and re-estimated values for two daily metrics, the daily maximum 8-h average (DM8A) and the daily 24-h average (D24A) ozone concentrations, were obtained with the OBS scenario using ozone observations only in contrast with the RAMP and a Constant Air Quality Model Performance (CAMP) scenarios. We show that, by accounting for the spatial and temporal variability in model performance, our novel RAMP approach is able to extract more information in terms of R(2) increase percentage, with over 12 times for the DM8A and over 3.5 times for the D24A ozone concentrations, from CTM predictions than the CAMP approach assuming that model performance does not change across space and time.
BAYESIAN DEMONSTRATION TEST METHOD WITH MIXED BETA DISTRIBUTION
Institute of Scientific and Technical Information of China (English)
MING Zhimao; TAO Junyong; CHEN Xun; ZHANG Yunan
2008-01-01
A complex mechatronics system Bayesian plan of demonstration test is studied based on the mixed beta distribution. During product design and improvement various information is appropriately considered by introducing inheritance factor, moreover, the inheritance factor is thought as a random variable, and the Bayesian decision of the qualification test plan is obtained, and the correctness of a Bayesian model presented is verified. The results show that the quantity of the test is too conservative according to classical methods under small binomial samples. Although traditional Bayesian analysis can consider test information of related or similar products, it ignores differences between such products. The method has solved the above problem, furthermore, considering the requirement in many practical projects, the differences among this method, the classical method and Bayesian with beta distribution are compared according to the plan of reliability acceptance test.
Metainference: A Bayesian Inference Method for Heterogeneous Systems
Bonomi, Massimiliano; Cavalli, Andrea; Vendruscolo, Michele
2015-01-01
Modelling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model, and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system populates simultaneously an ensemble of different states and experimental data are measured as averages over such states. To address this problem we present a method, called metainference, that combines Bayesian inference, which is a powerful strategy to deal with errors in experimental measurements, with the maximum entropy principle, which represents a rigorous approach to deal with experimental measurements averaged over multiple states. To illustrate the method we present its application to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to model complex systems with...
BONNSAI: correlated stellar observables in Bayesian methods
Schneider, F. R. N.; Castro, N.; Fossati, L.; Langer, N.; de Koter, A.
2017-02-01
In an era of large spectroscopic surveys of stars and big data, sophisticated statistical methods become more and more important in order to infer fundamental stellar parameters such as mass and age. Bayesian techniques are powerful methods because they can match all available observables simultaneously to stellar models while taking prior knowledge properly into account. However, in most cases it is assumed that observables are uncorrelated which is generally not the case. Here, we include correlations in the Bayesian code Bonnsai by incorporating the covariance matrix in the likelihood function. We derive a parametrisation of the covariance matrix that, in addition to classical uncertainties, only requires the specification of a correlation parameter that describes how observables co-vary. Our correlation parameter depends purely on the method with which observables have been determined and can be analytically derived in some cases. This approach therefore has the advantage that correlations can be accounted for even if information for them are not available in specific cases but are known in general. Because the new likelihood model is a better approximation of the data, the reliability and robustness of the inferred parameters are improved. We find that neglecting correlations biases the most likely values of inferred stellar parameters and affects the precision with which these parameters can be determined. The importance of these biases depends on the strength of the correlations and the uncertainties. For example, we apply our technique to massive OB stars, but emphasise that it is valid for any type of stars. For effective temperatures and surface gravities determined from atmosphere modelling, we find that masses can be underestimated on average by 0.5σ and mass uncertainties overestimated by a factor of about 2 when neglecting correlations. At the same time, the age precisions are underestimated over a wide range of stellar parameters. We conclude that
A Bayesian method for microseismic source inversion
Pugh, D. J.; White, R. S.; Christie, P. A. F.
2016-08-01
Earthquake source inversion is highly dependent on location determination and velocity models. Uncertainties in both the model parameters and the observations need to be rigorously incorporated into an inversion approach. Here, we show a probabilistic Bayesian method that allows formal inclusion of the uncertainties in the moment tensor inversion. This method allows the combination of different sets of far-field observations, such as P-wave and S-wave polarities and amplitude ratios, into one inversion. Additional observations can be included by deriving a suitable likelihood function from the uncertainties. This inversion produces samples from the source posterior probability distribution, including a best-fitting solution for the source mechanism and associated probability. The inversion can be constrained to the double-couple space or allowed to explore the gamut of moment tensor solutions, allowing volumetric and other non-double-couple components. The posterior probability of the double-couple and full moment tensor source models can be evaluated from the Bayesian evidence, using samples from the likelihood distributions for the two source models, producing an estimate of whether or not a source is double-couple. Such an approach is ideally suited to microseismic studies where there are many sources of uncertainty and it is often difficult to produce reliability estimates of the source mechanism, although this can be true of many other cases. Using full-waveform synthetic seismograms, we also show the effects of noise, location, network distribution and velocity model uncertainty on the source probability density function. The noise has the largest effect on the results, especially as it can affect other parts of the event processing. This uncertainty can lead to erroneous non-double-couple source probability distributions, even when no other uncertainties exist. Although including amplitude ratios can improve the constraint on the source probability
Bayesian data analysis in population ecology: motivations, methods, and benefits
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.
Approximation methods for efficient learning of Bayesian networks
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.
HEURISTIC DISCRETIZATION METHOD FOR BAYESIAN NETWORKS
Directory of Open Access Journals (Sweden)
Mariana D.C. Lima
2014-01-01
Full Text Available Bayesian Network (BN is a classification technique widely used in Artificial Intelligence. Its structure is a Direct Acyclic Graph (DAG used to model the association of categorical variables. However, in cases where the variables are numerical, a previous discretization is necessary. Discretization methods are usually based on a statistical approach using the data distribution, such as division by quartiles. In this article we present a discretization using a heuristic that identifies events called peak and valley. Genetic Algorithm was used to identify these events having the minimization of the error between the estimated average for BN and the actual value of the numeric variable output as the objective function. The BN has been modeled from a database of Bit’s Rate of Penetration of the Brazilian pre-salt layer with 5 numerical variables and one categorical variable, using the proposed discretization and the division of the data by the quartiles. The results show that the proposed heuristic discretization has higher accuracy than the quartiles discretization.
An Efficient Bayesian Iterative Method for Solving Linear Systems
Institute of Scientific and Technical Information of China (English)
Deng DING; Kin Sio FONG; Ka Hou CHAN
2012-01-01
This paper concerns with the statistical methods for solving general linear systems.After a brief review of Bayesian perspective for inverse problems,a new and efficient iterative method for general linear systems from a Bayesian perspective is proposed.The convergence of this iterative method is proved,and the corresponding error analysis is studied.Finally,numerical experiments are given to support the efficiency of this iterative method,and some conclusions are obtained.
Stochastic back analysis of permeability coefficient using generalized Bayesian method
Institute of Scientific and Technical Information of China (English)
Zheng Guilan; Wang Yuan; Wang Fei; Yang Jian
2008-01-01
Owing to the fact that the conventional deterministic back analysis of the permeability coefficient cannot reflect the uncertainties of parameters, including the hydraulic head at the boundary, the permeability coefficient and measured hydraulic head, a stochastic back analysis taking consideration of uncertainties of parameters was performed using the generalized Bayesian method. Based on the stochastic finite element method (SFEM) for a seepage field, the variable metric algorithm and the generalized Bayesian method, formulas for stochastic back analysis of the permeability coefficient were derived. A case study of seepage analysis of a sluice foundation was performed to illustrate the proposed method. The results indicate that, with the generalized Bayesian method that considers the uncertainties of measured hydraulic head, the permeability coefficient and the hydraulic head at the boundary, both the mean and standard deviation of the permeability coefficient can be obtained and the standard deviation is less than that obtained by the conventional Bayesian method. Therefore, the present method is valid and applicable.
Messier, Kyle P; Campbell, Ted; Bradley, Philip J; Serre, Marc L
2015-08-18
Radon ((222)Rn) is a naturally occurring chemically inert, colorless, and odorless radioactive gas produced from the decay of uranium ((238)U), which is ubiquitous in rocks and soils worldwide. Exposure to (222)Rn is likely the second leading cause of lung cancer after cigarette smoking via inhalation; however, exposure through untreated groundwater is also a contributing factor to both inhalation and ingestion routes. A land use regression (LUR) model for groundwater (222)Rn with anisotropic geological and (238)U based explanatory variables is developed, which helps elucidate the factors contributing to elevated (222)Rn across North Carolina. The LUR is also integrated into the Bayesian Maximum Entropy (BME) geostatistical framework to increase accuracy and produce a point-level LUR-BME model of groundwater (222)Rn across North Carolina including prediction uncertainty. The LUR-BME model of groundwater (222)Rn results in a leave-one out cross-validation r(2) of 0.46 (Pearson correlation coefficient = 0.68), effectively predicting within the spatial covariance range. Modeled results of (222)Rn concentrations show variability among intrusive felsic geological formations likely due to average bedrock (238)U defined on the basis of overlying stream-sediment (238)U concentrations that is a widely distributed consistently analyzed point-source data.
Application of Bayesian Network Learning Methods to Land Resource Evaluation
Institute of Scientific and Technical Information of China (English)
HUANG Jiejun; HE Xiaorong; WAN Youchuan
2006-01-01
Bayesian network has a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to deal with prediction, classification and clustering. Firstly, this paper presented an overview of Bayesian network and its characteristics, and discussed how to learn a Bayesian network structure from given data, and then constructed a Bayesian network model for land resource evaluation with expert knowledge and the dataset. The experimental results based on the test dataset are that evaluation accuracy is 87.5%, and Kappa index is 0.826. All these prove the method is feasible and efficient, and indicate that Bayesian network is a promising approach for land resource evaluation.
Unification of field theory and maximum entropy methods for learning probability densities.
Kinney, Justin B
2015-09-01
The need to estimate smooth probability distributions (a.k.a. probability densities) from finite sampled data is ubiquitous in science. Many approaches to this problem have been described, but none is yet regarded as providing a definitive solution. Maximum entropy estimation and Bayesian field theory are two such approaches. Both have origins in statistical physics, but the relationship between them has remained unclear. Here I unify these two methods by showing that every maximum entropy density estimate can be recovered in the infinite smoothness limit of an appropriate Bayesian field theory. I also show that Bayesian field theory estimation can be performed without imposing any boundary conditions on candidate densities, and that the infinite smoothness limit of these theories recovers the most common types of maximum entropy estimates. Bayesian field theory thus provides a natural test of the maximum entropy null hypothesis and, furthermore, returns an alternative (lower entropy) density estimate when the maximum entropy hypothesis is falsified. The computations necessary for this approach can be performed rapidly for one-dimensional data, and software for doing this is provided.
Radiation Source Mapping with Bayesian Inverse Methods
Hykes, Joshua Michael
We present a method to map the spectral and spatial distributions of radioactive sources using a small number of detectors. Locating and identifying radioactive materials is important for border monitoring, accounting for special nuclear material in processing facilities, and in clean-up operations. Most methods to analyze these problems make restrictive assumptions about the distribution of the source. In contrast, the source-mapping method presented here allows an arbitrary three-dimensional distribution in space and a flexible group and gamma peak distribution in energy. To apply the method, the system's geometry and materials must be known. A probabilistic Bayesian approach is used to solve the resulting inverse problem (IP) since the system of equations is ill-posed. The probabilistic approach also provides estimates of the confidence in the final source map prediction. A set of adjoint flux, discrete ordinates solutions, obtained in this work by the Denovo code, are required to efficiently compute detector responses from a candidate source distribution. These adjoint fluxes are then used to form the linear model to map the state space to the response space. The test for the method is simultaneously locating a set of 137Cs and 60Co gamma sources in an empty room. This test problem is solved using synthetic measurements generated by a Monte Carlo (MCNP) model and using experimental measurements that we collected for this purpose. With the synthetic data, the predicted source distributions identified the locations of the sources to within tens of centimeters, in a room with an approximately four-by-four meter floor plan. Most of the predicted source intensities were within a factor of ten of their true value. The chi-square value of the predicted source was within a factor of five from the expected value based on the number of measurements employed. With a favorable uniform initial guess, the predicted source map was nearly identical to the true distribution
Advanced Bayesian Methods for Lunar Surface Navigation Project
National Aeronautics and Space Administration — The key innovation of this project is the application of advanced Bayesian methods to integrate real-time dense stereo vision and high-speed optical flow with an...
Advanced Bayesian Methods for Lunar Surface Navigation Project
National Aeronautics and Space Administration — The key innovation of this project will be the application of advanced Bayesian methods to integrate real-time dense stereo vision and high-speed optical flow with...
Proceedings of the First Astrostatistics School: Bayesian Methods in Cosmology
Hortúa, Héctor J
2014-01-01
These are the proceedings of the First Astrostatistics School: Bayesian Methods in Cosmology, held in Bogot\\'a D.C., Colombia, June 9-13, 2014. The first astrostatistics school has been the first event in Colombia where statisticians and cosmologists from some universities in Bogot\\'a met to discuss the statistic methods applied to cosmology, especially the use of Bayesian statistics in the study of Cosmic Microwave Background (CMB), Baryonic Acoustic Oscillations (BAO), Large Scale Structure (LSS) and weak lensing.
An overview of component qualification using Bayesian statistics and energy methods.
Energy Technology Data Exchange (ETDEWEB)
Dohner, Jeffrey Lynn
2011-09-01
The below overview is designed to give the reader a limited understanding of Bayesian and Maximum Likelihood (MLE) estimation; a basic understanding of some of the mathematical tools to evaluate the quality of an estimation; an introduction to energy methods and a limited discussion of damage potential. This discussion then goes on to presented a limited presentation as to how energy methods and Bayesian estimation are used together to qualify components. Example problems with solutions have been supplied as a learning aid. Bold letters are used to represent random variables. Un-bolded letter represent deterministic values. A concluding section presents a discussion of attributes and concerns.
Curtis, Gary P.; Lu, Dan; Ye, Ming
2015-01-01
While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the
Fiebig, H R
2002-01-01
We study various aspects of extracting spectral information from time correlation functions of lattice QCD by means of Bayesian inference with an entropic prior, the maximum entropy method (MEM). Correlator functions of a heavy-light meson-meson system serve as a repository for lattice data with diverse statistical quality. Attention is given to spectral mass density functions, inferred from the data, and their dependence on the parameters of the MEM. We propose to employ simulated annealing, or cooling, to solve the Bayesian inference problem, and discuss practical issues of the approach.
Bayesian methods for the design and analysis of noninferiority trials.
Gamalo-Siebers, Margaret; Gao, Aijun; Lakshminarayanan, Mani; Liu, Guanghan; Natanegara, Fanni; Railkar, Radha; Schmidli, Heinz; Song, Guochen
2016-01-01
The gold standard for evaluating treatment efficacy of a medical product is a placebo-controlled trial. However, when the use of placebo is considered to be unethical or impractical, a viable alternative for evaluating treatment efficacy is through a noninferiority (NI) study where a test treatment is compared to an active control treatment. The minimal objective of such a study is to determine whether the test treatment is superior to placebo. An assumption is made that if the active control treatment remains efficacious, as was observed when it was compared against placebo, then a test treatment that has comparable efficacy with the active control, within a certain range, must also be superior to placebo. Because of this assumption, the design, implementation, and analysis of NI trials present challenges for sponsors and regulators. In designing and analyzing NI trials, substantial historical data are often required on the active control treatment and placebo. Bayesian approaches provide a natural framework for synthesizing the historical data in the form of prior distributions that can effectively be used in design and analysis of a NI clinical trial. Despite a flurry of recent research activities in the area of Bayesian approaches in medical product development, there are still substantial gaps in recognition and acceptance of Bayesian approaches in NI trial design and analysis. The Bayesian Scientific Working Group of the Drug Information Association provides a coordinated effort to target the education and implementation issues on Bayesian approaches for NI trials. In this article, we provide a review of both frequentist and Bayesian approaches in NI trials, and elaborate on the implementation for two common Bayesian methods including hierarchical prior method and meta-analytic-predictive approach. Simulations are conducted to investigate the properties of the Bayesian methods, and some real clinical trial examples are presented for illustration.
Wang, Chaolin; Zhong, Shaobo; Zhang, Fushen; Huang, Quanyi
2016-11-01
Precipitation interpolation has been a hot area of research for many years. It had close relation to meteorological factors. In this paper, precipitation from 91 meteorological stations located in and around Yunnan, Guizhou and Guangxi Zhuang provinces (or autonomous region), Mainland China was taken into consideration for spatial interpolation. Multivariate Bayesian maximum entropy (BME) method with auxiliary variables, including mean relative humidity, water vapour pressure, mean temperature, mean wind speed and terrain elevation, was used to get more accurate regional distribution of annual precipitation. The means, standard deviations, skewness and kurtosis of meteorological factors were calculated. Variogram and cross- variogram were fitted between precipitation and auxiliary variables. The results showed that the multivariate BME method was precise with hard and soft data, probability density function. Annual mean precipitation was positively correlated with mean relative humidity, mean water vapour pressure, mean temperature and mean wind speed, negatively correlated with terrain elevation. The results are supposed to provide substantial reference for research of drought and waterlog in the region.
Directory of Open Access Journals (Sweden)
Gogarten J Peter
2002-02-01
Full Text Available Abstract Background Horizontal gene transfer (HGT played an important role in shaping microbial genomes. In addition to genes under sporadic selection, HGT also affects housekeeping genes and those involved in information processing, even ribosomal RNA encoding genes. Here we describe tools that provide an assessment and graphic illustration of the mosaic nature of microbial genomes. Results We adapted the Maximum Likelihood (ML mapping to the analyses of all detected quartets of orthologous genes found in four genomes. We have automated the assembly and analyses of these quartets of orthologs given the selection of four genomes. We compared the ML-mapping approach to more rigorous Bayesian probability and Bootstrap mapping techniques. The latter two approaches appear to be more conservative than the ML-mapping approach, but qualitatively all three approaches give equivalent results. All three tools were tested on mitochondrial genomes, which presumably were inherited as a single linkage group. Conclusions In some instances of interphylum relationships we find nearly equal numbers of quartets strongly supporting the three possible topologies. In contrast, our analyses of genome quartets containing the cyanobacterium Synechocystis sp. indicate that a large part of the cyanobacterial genome is related to that of low GC Gram positives. Other groups that had been suggested as sister groups to the cyanobacteria contain many fewer genes that group with the Synechocystis orthologs. Interdomain comparisons of genome quartets containing the archaeon Halobacterium sp. revealed that Halobacterium sp. shares more genes with Bacteria that live in the same environment than with Bacteria that are more closely related based on rRNA phylogeny . Many of these genes encode proteins involved in substrate transport and metabolism and in information storage and processing. The performed analyses demonstrate that relationships among prokaryotes cannot be accurately
de Nazelle, Audrey; Arunachalam, Saravanan; Serre, Marc L
2010-08-01
States in the USA are required to demonstrate future compliance of criteria air pollutant standards by using both air quality monitors and model outputs. In the case of ozone, the demonstration tests aim at relying heavily on measured values, due to their perceived objectivity and enforceable quality. Weight given to numerical models is diminished by integrating them in the calculations only in a relative sense. For unmonitored locations, the EPA has suggested the use of a spatial interpolation technique to assign current values. We demonstrate that this approach may lead to erroneous assignments of nonattainment and may make it difficult for States to establish future compliance. We propose a method that combines different sources of information to map air pollution, using the Bayesian Maximum Entropy (BME) Framework. The approach gives precedence to measured values and integrates modeled data as a function of model performance. We demonstrate this approach in North Carolina, using the State's ozone monitoring network in combination with outputs from the Multiscale Air Quality Simulation Platform (MAQSIP) modeling system. We show that the BME data integration approach, compared to a spatial interpolation of measured data, improves the accuracy and the precision of ozone estimations across the state.
Hu, Junguo; Zhou, Jian; Zhou, Guomo; Luo, Yiqi; Xu, Xiaojun; Li, Pingheng; Liang, Junyi
2016-01-01
Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.
A dual method for maximum entropy restoration
Smith, C. B.
1979-01-01
A simple iterative dual algorithm for maximum entropy image restoration is presented. The dual algorithm involves fewer parameters than conventional minimization in the image space. Minicomputer test results for Fourier synthesis with inadequate phantom data are given.
A Comparison of Imputation Methods for Bayesian Factor Analysis Models
Merkle, Edgar C.
2011-01-01
Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted…
Stochastic back analysis of permeability coefficient using generalized Bayesian method
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Gui-lan ZHENG
2008-09-01
Full Text Available Owing to the fact that the conventional deterministic back analysis of the permeability coefficient cannot reflect the uncertainties of parameters, including the hydraulic head at the boundary, the permeability coefficient and measured hydraulic head, a stochastic back analysis taking consideration of uncertainties of parameters was performed using the generalized Bayesian method. Based on the stochastic finite element method (SFEM for a seepage field, the variable metric algorithm and the generalized Bayesian method, formulas for stochastic back analysis of the permeability coefficient were derived. A case study of seepage analysis of a sluice foundation was performed to illustrate the proposed method. The results indicate that, with the generalized Bayesian method that considers the uncertainties of measured hydraulic head, the permeability coefficient and the hydraulic head at the boundary, both the mean and standard deviation of the permeability coefficient can be obtained and the standard deviation is less than that obtained by the conventional Bayesian method. Therefore, the present method is valid and applicable.
Bayesian methods to estimate urban growth potential
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.
Bayesian Monte Carlo Method for Nuclear Data Evaluation
Energy Technology Data Exchange (ETDEWEB)
Koning, A.J., E-mail: koning@nrg.eu
2015-01-15
A Bayesian Monte Carlo method is outlined which allows a systematic evaluation of nuclear reactions using TALYS. The result will be either an EXFOR-weighted covariance matrix or a collection of random files, each accompanied by an experiment based weight.
Maximum-entropy weak lens reconstruction improved methods and application to data
Marshall, P J; Gull, S F; Bridle, S L
2002-01-01
We develop the maximum-entropy weak shear mass reconstruction method presented in earlier papers by taking each background galaxy image shape as an independent estimator of the reduced shear field and incorporating an intrinsic smoothness into the reconstruction. The characteristic length scale of this smoothing is determined by Bayesian methods. Within this algorithm the uncertainties due to the intrinsic distribution of galaxy shapes are carried through to the final mass reconstruction, and the mass within arbitrarily shaped apertures can be calculated with corresponding uncertainties. We apply this method to two clusters taken from N-body simulations using mock observations corresponding to Keck LRIS and mosaiced HST WFPC2 fields. We demonstrate that the Bayesian choice of smoothing length is sensible and that masses within apertures (including one on a filamentary structure) are reliable. We apply the method to data taken on the cluster MS1054-03 using the Keck LRIS (Clowe et al. 2000) and HST (Hoekstra e...
A label field fusion bayesian model and its penalized maximum rand estimator for image segmentation.
Mignotte, Max
2010-06-01
This paper presents a novel segmentation approach based on a Markov random field (MRF) fusion model which aims at combining several segmentation results associated with simpler clustering models in order to achieve a more reliable and accurate segmentation result. The proposed fusion model is derived from the recently introduced probabilistic Rand measure for comparing one segmentation result to one or more manual segmentations of the same image. This non-parametric measure allows us to easily derive an appealing fusion model of label fields, easily expressed as a Gibbs distribution, or as a nonstationary MRF model defined on a complete graph. Concretely, this Gibbs energy model encodes the set of binary constraints, in terms of pairs of pixel labels, provided by each segmentation results to be fused. Combined with a prior distribution, this energy-based Gibbs model also allows for definition of an interesting penalized maximum probabilistic rand estimator with which the fusion of simple, quickly estimated, segmentation results appears as an interesting alternative to complex segmentation models existing in the literature. This fusion framework has been successfully applied on the Berkeley image database. The experiments reported in this paper demonstrate that the proposed method is efficient in terms of visual evaluation and quantitative performance measures and performs well compared to the best existing state-of-the-art segmentation methods recently proposed in the literature.
Application of an efficient Bayesian discretization method to biomedical data
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Gopalakrishnan Vanathi
2011-07-01
Full Text Available Abstract Background Several data mining methods require data that are discrete, and other methods often perform better with discrete data. We introduce an efficient Bayesian discretization (EBD method for optimal discretization of variables that runs efficiently on high-dimensional biomedical datasets. The EBD method consists of two components, namely, a Bayesian score to evaluate discretizations and a dynamic programming search procedure to efficiently search the space of possible discretizations. We compared the performance of EBD to Fayyad and Irani's (FI discretization method, which is commonly used for discretization. Results On 24 biomedical datasets obtained from high-throughput transcriptomic and proteomic studies, the classification performances of the C4.5 classifier and the naïve Bayes classifier were statistically significantly better when the predictor variables were discretized using EBD over FI. EBD was statistically significantly more stable to the variability of the datasets than FI. However, EBD was less robust, though not statistically significantly so, than FI and produced slightly more complex discretizations than FI. Conclusions On a range of biomedical datasets, a Bayesian discretization method (EBD yielded better classification performance and stability but was less robust than the widely used FI discretization method. The EBD discretization method is easy to implement, permits the incorporation of prior knowledge and belief, and is sufficiently fast for application to high-dimensional data.
Involving stakeholders in building integrated fisheries models using Bayesian methods.
Haapasaari, Päivi; Mäntyniemi, Samu; Kuikka, Sakari
2013-06-01
A participatory Bayesian approach was used to investigate how the views of stakeholders could be utilized to develop models to help understand the Central Baltic herring fishery. In task one, we applied the Bayesian belief network methodology to elicit the causal assumptions of six stakeholders on factors that influence natural mortality, growth, and egg survival of the herring stock in probabilistic terms. We also integrated the expressed views into a meta-model using the Bayesian model averaging (BMA) method. In task two, we used influence diagrams to study qualitatively how the stakeholders frame the management problem of the herring fishery and elucidate what kind of causalities the different views involve. The paper combines these two tasks to assess the suitability of the methodological choices to participatory modeling in terms of both a modeling tool and participation mode. The paper also assesses the potential of the study to contribute to the development of participatory modeling practices. It is concluded that the subjective perspective to knowledge, that is fundamental in Bayesian theory, suits participatory modeling better than a positivist paradigm that seeks the objective truth. The methodology provides a flexible tool that can be adapted to different kinds of needs and challenges of participatory modeling. The ability of the approach to deal with small data sets makes it cost-effective in participatory contexts. However, the BMA methodology used in modeling the biological uncertainties is so complex that it needs further development before it can be introduced to wider use in participatory contexts.
Involving Stakeholders in Building Integrated Fisheries Models Using Bayesian Methods
Haapasaari, Päivi; Mäntyniemi, Samu; Kuikka, Sakari
2013-06-01
A participatory Bayesian approach was used to investigate how the views of stakeholders could be utilized to develop models to help understand the Central Baltic herring fishery. In task one, we applied the Bayesian belief network methodology to elicit the causal assumptions of six stakeholders on factors that influence natural mortality, growth, and egg survival of the herring stock in probabilistic terms. We also integrated the expressed views into a meta-model using the Bayesian model averaging (BMA) method. In task two, we used influence diagrams to study qualitatively how the stakeholders frame the management problem of the herring fishery and elucidate what kind of causalities the different views involve. The paper combines these two tasks to assess the suitability of the methodological choices to participatory modeling in terms of both a modeling tool and participation mode. The paper also assesses the potential of the study to contribute to the development of participatory modeling practices. It is concluded that the subjective perspective to knowledge, that is fundamental in Bayesian theory, suits participatory modeling better than a positivist paradigm that seeks the objective truth. The methodology provides a flexible tool that can be adapted to different kinds of needs and challenges of participatory modeling. The ability of the approach to deal with small data sets makes it cost-effective in participatory contexts. However, the BMA methodology used in modeling the biological uncertainties is so complex that it needs further development before it can be introduced to wider use in participatory contexts.
Lee, Chieh-Han; Yu, Hwa-Lung; Chien, Lung-Chang
2014-05-01
Dengue fever has been identified as one of the most widespread vector-borne diseases in tropical and sub-tropical. In the last decade, dengue is an emerging infectious disease epidemic in Taiwan especially in the southern area where have annually high incidences. For the purpose of disease prevention and control, an early warning system is urgently needed. Previous studies have showed significant relationships between climate variables, in particular, rainfall and temperature, and the temporal epidemic patterns of dengue cases. However, the transmission of the dengue fever is a complex interactive process that mostly understated the composite space-time effects of dengue fever. This study proposes developing a one-week ahead warning system of dengue fever epidemics in the southern Taiwan that considered nonlinear associations between weekly dengue cases and meteorological factors across space and time. The early warning system based on an integration of distributed lag nonlinear model (DLNM) and stochastic Bayesian Maximum Entropy (BME) analysis. The study identified the most significant meteorological measures including weekly minimum temperature and maximum 24-hour rainfall with continuous 15-week lagged time to dengue cases variation under condition of uncertainty. Subsequently, the combination of nonlinear lagged effects of climate variables and space-time dependence function is implemented via a Bayesian framework to predict dengue fever occurrences in the southern Taiwan during 2012. The result shows the early warning system is useful for providing potential outbreak spatio-temporal prediction of dengue fever distribution. In conclusion, the proposed approach can provide a practical disease control tool for environmental regulators seeking more effective strategies for dengue fever prevention.
An Overview of Bayesian Methods for Neural Spike Train Analysis
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Zhe Chen
2013-01-01
Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.
Bayesian Methods for Analysis and Adaptive Scheduling of Exoplanet Observations
Loredo, Thomas J; Chernoff, David F; Clyde, Merlise A; Liu, Bin
2011-01-01
We describe work in progress by a collaboration of astronomers and statisticians developing a suite of Bayesian data analysis tools for extrasolar planet (exoplanet) detection, planetary orbit estimation, and adaptive scheduling of observations. Our work addresses analysis of stellar reflex motion data, where a planet is detected by observing the "wobble" of its host star as it responds to the gravitational tug of the orbiting planet. Newtonian mechanics specifies an analytical model for the resulting time series, but it is strongly nonlinear, yielding complex, multimodal likelihood functions; it is even more complex when multiple planets are present. The parameter spaces range in size from few-dimensional to dozens of dimensions, depending on the number of planets in the system, and the type of motion measured (line-of-sight velocity, or position on the sky). Since orbits are periodic, Bayesian generalizations of periodogram methods facilitate the analysis. This relies on the model being linearly separable, ...
Bayesian analysis of the flutter margin method in aeroelasticity
Khalil, Mohammad; Poirel, Dominique; Sarkar, Abhijit
2016-12-01
A Bayesian statistical framework is presented for Zimmerman and Weissenburger flutter margin method which considers the uncertainties in aeroelastic modal parameters. The proposed methodology overcomes the limitations of the previously developed least-square based estimation technique which relies on the Gaussian approximation of the flutter margin probability density function (pdf). Using the measured free-decay responses at subcritical (preflutter) airspeeds, the joint non-Gaussain posterior pdf of the modal parameters is sampled using the Metropolis-Hastings (MH) Markov chain Monte Carlo (MCMC) algorithm. The posterior MCMC samples of the modal parameters are then used to obtain the flutter margin pdfs and finally the flutter speed pdf. The usefulness of the Bayesian flutter margin method is demonstrated using synthetic data generated from a two-degree-of-freedom pitch-plunge aeroelastic model. The robustness of the statistical framework is demonstrated using different sets of measurement data. It will be shown that the probabilistic (Bayesian) approach reduces the number of test points required in providing a flutter speed estimate for a given accuracy and precision.
An Interval Maximum Entropy Method for Quadratic Programming Problem
Institute of Scientific and Technical Information of China (English)
RUI Wen-juan; CAO De-xin; SONG Xie-wu
2005-01-01
With the idea of maximum entropy function and penalty function methods, we transform the quadratic programming problem into an unconstrained differentiable optimization problem, discuss the interval extension of the maximum entropy function, provide the region deletion test rules and design an interval maximum entropy algorithm for quadratic programming problem. The convergence of the method is proved and numerical results are presented. Both theoretical and numerical results show that the method is reliable and efficient.
Approach to the Correlation Discovery of Chinese Linguistic Parameters Based on Bayesian Method
Institute of Scientific and Technical Information of China (English)
WANG Wei(王玮); CAI LianHong(蔡莲红)
2003-01-01
Bayesian approach is an important method in statistics. The Bayesian belief network is a powerful knowledge representation and reasoning tool under the conditions of uncertainty.It is a graphics model that encodes probabilistic relationships among variables of interest. In this paper, an approach to Bayesian network construction is given for discovering the Chinese linguistic parameter relationship in the corpus.
Adam-Poupart, Ariane; Brand, Allan; Fournier, Michel; Jerrett, Michael
2014-01-01
Background: Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. Objectives: We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. Methods: We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. Results: The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Conclusions: Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data. Citation: Adam-Poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A. 2014. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy–LUR approaches. Environ Health Perspect 122:970–976; http://dx.doi.org/10.1289/ehp.1306566 PMID:24879650
An improved Bayesian matting method based on image statistic characteristics
Sun, Wei; Luo, Siwei; Wu, Lina
2015-03-01
Image matting is an important task in image and video editing and has been studied for more than 30 years. In this paper we propose an improved interactive matting method. Starting from a coarse user-guided trimap, we first perform a color estimation based on texture and color information and use the result to refine the original trimap. Then with the new trimap, we apply soft matting process which is improved Bayesian matting with smoothness constraints. Experimental results on natural image show that this method is useful, especially for the images have similar texture feature in the background or the images which is hard to give a precise trimap.
Marmon, Livingstone
2013-12-01
Uptake of ferric siderophores, vitamin B12, and other molecules in gram-negative bacteria is mediated by a multi-protein complex known as the TonB system. The ExbB and ExbD protein components of the TonB system play key energizing roles and are homologous with the flagellar motor proteins MotA and MotB. Here, the phylogenetic relationships of ExbBD and MotAB were investigated using Bayesian inference and the maximum-likelihood method. Phylogenetic trees of these proteins suggest that they are separated into distinct monophyletic groups and have originated from a common ancestral system. Several horizontal gene transfer events for ExbB-ExbD are also inferred, and a model for the evolution of the TonB system is proposed. Copyright © 2013 Elsevier Inc. All rights reserved.
Bayesian Methods for Nonlinear System Identification of Civil Structures
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Conte Joel P.
2015-01-01
Full Text Available This paper presents a new framework for the identification of mechanics-based nonlinear finite element (FE models of civil structures using Bayesian methods. In this approach, recursive Bayesian estimation methods are utilized to update an advanced nonlinear FE model of the structure using the input-output dynamic data recorded during an earthquake event. Capable of capturing the complex damage mechanisms and failure modes of the structural system, the updated nonlinear FE model can be used to evaluate the state of health of the structure after a damage-inducing event. To update the unknown time-invariant parameters of the FE model, three alternative stochastic filtering methods are used: the extended Kalman filter (EKF, the unscented Kalman filter (UKF, and the iterated extended Kalman filter (IEKF. For those estimation methods that require the computation of structural FE response sensitivities with respect to the unknown modeling parameters (EKF and IEKF, the accurate and computationally efficient direct differentiation method (DDM is used. A three-dimensional five-story two-by-one bay reinforced concrete (RC frame is used to illustrate the performance of the framework and compare the performance of the different filters in terms of convergence, accuracy, and robustness. Excellent estimation results are obtained with the UKF, EKF, and IEKF. Because of the analytical linearization used in the EKF and IEKF, abrupt and large jumps in the estimates of the modeling parameters are observed when using these filters. The UKF slightly outperforms the EKF and IEKF.
Zhang, Liangcai; Yuan, Ying
2016-11-30
Drug combination therapy has become the mainstream approach to cancer treatment. One fundamental feature that makes combination trials different from single-agent trials is the existence of the maximum tolerated dose (MTD) contour, that is, multiple MTDs. As a result, unlike single-agent phase I trials, which aim to find a single MTD, it is often of interest to find the MTD contour for combination trials. We propose a new dose-finding design, the waterfall design, to find the MTD contour for drug combination trials. Taking the divide-and-conquer strategy, the waterfall design divides the task of finding the MTD contour into a sequence of one-dimensional dose-finding processes, known as subtrials. The subtrials are conducted sequentially in a certain order, such that the results of each subtrial will be used to inform the design of subsequent subtrials. Such information borrowing allows the waterfall design to explore the two-dimensional dose space efficiently using a limited sample size and decreases the chance of overdosing and underdosing patients. To accommodate the consideration that doses on the MTD contour may have very different efficacy or synergistic effects because of drug-drug interaction, we further extend our approach to a phase I/II design with the goal of finding the MTD with the highest efficacy. Simulation studies show that the waterfall design is safer and has higher probability of identifying the true MTD contour than some existing designs. The R package "BOIN" to implement the waterfall design is freely available from CRAN. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
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.
A Bayesian nonparametric method for prediction in EST analysis
Directory of Open Access Journals (Sweden)
Prünster Igor
2007-09-01
Full Text Available Abstract Background Expressed sequence tags (ESTs analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the gene discovery rate: these estimates represent the basis for deciding whether to proceed sequencing the library and, in case of a positive decision, a guideline for selecting the size of the new sample. Such information is also useful for establishing sequencing efficiency in experimental design and for measuring the degree of redundancy of an EST library. Results In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a the coverage, defined as the proportion of unique genes in the library represented in the given sample of reads; b the number of new unique genes to be observed in a future sample; c the discovery rate of new genes as a function of the future sample size. The Bayesian nonparametric model we adopt conveys, in a statistically rigorous way, the available information into prediction. Our proposal has appealing properties over frequentist nonparametric methods, which become unstable when prediction is required for large future samples. EST libraries, previously studied with frequentist methods, are analyzed in detail. Conclusion The Bayesian nonparametric approach we undertake yields valuable tools for gene capture and prediction in EST libraries. The estimators we obtain do not feature the kind of drawbacks associated with frequentist estimators and are reliable for any size of the additional sample.
Distance and extinction determination for APOGEE stars with Bayesian method
Wang, Jianling; Pan, Kaike; Chen, Bingqiu; Zhao, Yongheng; Wicker, James
2016-01-01
Using a Bayesian technology we derived distances and extinctions for over 100,000 red giant stars observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey by taking into account spectroscopic constraints from the APOGEE stellar parameters and photometric constraints from 2MASS, as well as a prior knowledge on the Milky Way. Derived distances are compared with those from four other independent methods, the Hipparcos parallaxes, star clusters, APOGEE red clump stars, and asteroseismic distances from APOKASC (Rodrigues et al. 2014) and SAGA Catalogues (Casagrande et al. 2014). These comparisons covers four orders of magnitude in the distance scale from 0.02 kpc to 20 kpc. The results show that our distances agree very well with those from other methods: the mean relative difference between our Bayesian distances and those derived from other methods ranges from -4.2% to +3.6%, and the dispersion ranges from 15% to 25%. The extinctions toward all stars are also derived and compared wi...
Constructive Epistemic Modeling: A Hierarchical Bayesian Model Averaging Method
Tsai, F. T. C.; Elshall, A. S.
2014-12-01
Constructive epistemic modeling is the idea that our understanding of a natural system through a scientific model is a mental construct that continually develops through learning about and from the model. Using the hierarchical Bayesian model averaging (HBMA) method [1], this study shows that segregating different uncertain model components through a BMA tree of posterior model probabilities, model prediction, within-model variance, between-model variance and total model variance serves as a learning tool [2]. First, the BMA tree of posterior model probabilities permits the comparative evaluation of the candidate propositions of each uncertain model component. Second, systemic model dissection is imperative for understanding the individual contribution of each uncertain model component to the model prediction and variance. Third, the hierarchical representation of the between-model variance facilitates the prioritization of the contribution of each uncertain model component to the overall model uncertainty. We illustrate these concepts using the groundwater modeling of a siliciclastic aquifer-fault system. The sources of uncertainty considered are from geological architecture, formation dip, boundary conditions and model parameters. The study shows that the HBMA analysis helps in advancing knowledge about the model rather than forcing the model to fit a particularly understanding or merely averaging several candidate models. [1] Tsai, F. T.-C., and A. S. Elshall (2013), Hierarchical Bayesian model averaging for hydrostratigraphic modeling: Uncertainty segregation and comparative evaluation. Water Resources Research, 49, 5520-5536, doi:10.1002/wrcr.20428. [2] Elshall, A.S., and F. T.-C. Tsai (2014). Constructive epistemic modeling of groundwater flow with geological architecture and boundary condition uncertainty under Bayesian paradigm, Journal of Hydrology, 517, 105-119, doi: 10.1016/j.jhydrol.2014.05.027.
Bayesian Monte Carlo method for nuclear data evaluation
Energy Technology Data Exchange (ETDEWEB)
Koning, A.J. [Nuclear Research and Consultancy Group NRG, P.O. Box 25, ZG Petten (Netherlands)
2015-12-15
A Bayesian Monte Carlo method is outlined which allows a systematic evaluation of nuclear reactions using the nuclear model code TALYS and the experimental nuclear reaction database EXFOR. The method is applied to all nuclides at the same time. First, the global predictive power of TALYS is numerically assessed, which enables to set the prior space of nuclear model solutions. Next, the method gradually zooms in on particular experimental data per nuclide, until for each specific target nuclide its existing experimental data can be used for weighted Monte Carlo sampling. To connect to the various different schools of uncertainty propagation in applied nuclear science, the result will be either an EXFOR-weighted covariance matrix or a collection of random files, each accompanied by the EXFOR-based weight. (orig.)
Bayesian Monte Carlo method for nuclear data evaluation
Koning, A. J.
2015-12-01
A Bayesian Monte Carlo method is outlined which allows a systematic evaluation of nuclear reactions using the nuclear model code TALYS and the experimental nuclear reaction database EXFOR. The method is applied to all nuclides at the same time. First, the global predictive power of TALYS is numerically assessed, which enables to set the prior space of nuclear model solutions. Next, the method gradually zooms in on particular experimental data per nuclide, until for each specific target nuclide its existing experimental data can be used for weighted Monte Carlo sampling. To connect to the various different schools of uncertainty propagation in applied nuclear science, the result will be either an EXFOR-weighted covariance matrix or a collection of random files, each accompanied by the EXFOR-based weight.
Bayesian statistic methods and theri application in probabilistic simulation models
Directory of Open Access Journals (Sweden)
Sergio Iannazzo
2007-03-01
Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.
Bayesian structural equation modeling method for hierarchical model validation
Energy Technology Data Exchange (ETDEWEB)
Jiang Xiaomo [Department of Civil and Environmental Engineering, Vanderbilt University, Box 1831-B, Nashville, TN 37235 (United States)], E-mail: xiaomo.jiang@vanderbilt.edu; Mahadevan, Sankaran [Department of Civil and Environmental Engineering, Vanderbilt University, Box 1831-B, Nashville, TN 37235 (United States)], E-mail: sankaran.mahadevan@vanderbilt.edu
2009-04-15
A building block approach to model validation may proceed through various levels, such as material to component to subsystem to system, comparing model predictions with experimental observations at each level. Usually, experimental data becomes scarce as one proceeds from lower to higher levels. This paper presents a structural equation modeling approach to make use of the lower-level data for higher-level model validation under uncertainty, integrating several components: lower-level data, higher-level data, computational model, and latent variables. The method proposed in this paper uses latent variables to model two sets of relationships, namely, the computational model to system-level data, and lower-level data to system-level data. A Bayesian network with Markov chain Monte Carlo simulation is applied to represent the two relationships and to estimate the influencing factors between them. Bayesian hypothesis testing is employed to quantify the confidence in the predictive model at the system level, and the role of lower-level data in the model validation assessment at the system level. The proposed methodology is implemented for hierarchical assessment of three validation problems, using discrete observations and time-series data.
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A Bayesian approach using Markov chain Monte Carlo algorithms has been developed to analyze Smith's discretized version of the discovery process model. It avoids the problems involved in the maximum likelihood method by effectively making use of the information from the prior distribution and that from the discovery sequence according to posterior probabilities. All statistical inferences about the parameters of the model and total resources can be quantified by drawing samples directly from the joint posterior distribution. In addition, statistical errors of the samples can be easily assessed and the convergence properties can be monitored during the sampling. Because the information contained in a discovery sequence is not enough to estimate all parameters, especially the number of fields, geologically justified prior information is crucial to the estimation. The Bayesian approach allows the analyst to specify his subjective estimates of the required parameters and his degree of uncertainty about the estimates in a clearly identified fashion throughout the analysis. As an example, this approach is applied to the same data of the North Sea on which Smith demonstrated his maximum likelihood method. For this case, the Bayesian approach has really improved the overly pessimistic results and downward bias of the maximum likelihood procedure.
A MAXIMUM ENTROPY METHOD FOR CONSTRAINED SEMI-INFINITEPROGRAMMING PROBLEMS
Institute of Scientific and Technical Information of China (English)
ZHOU Guanglu; WANG Changyu; SHI Zhenjun; SUN Qingying
1999-01-01
This paper presents a new method, called the maximum entropy method,for solving semi-infinite programming problems, in which thesemi-infinite programming problem is approximated by one with a singleconstraint. The convergence properties for this method are discussed.Numerical examples are given to show the high effciency of thealgorithm.
Bayesian methods in the search for MH370
Davey, Sam; Holland, Ian; Rutten, Mark; Williams, Jason
2016-01-01
This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was used to define the search zone in the southern Indian Ocean. The book describes particle-filter based numerical calculation of the aircraft flight-path probability distribution and validates the method using data from several of the involved aircraft’s previous flights. Finally it is shown how the Reunion Island flaperon debris find affects the search probability distribution.
QUEST+: A general multidimensional Bayesian adaptive psychometric method.
Watson, Andrew B
2017-03-01
QUEST+ is a Bayesian adaptive psychometric testing method that allows an arbitrary number of stimulus dimensions, psychometric function parameters, and trial outcomes. It is a generalization and extension of the original QUEST procedure and incorporates many subsequent developments in the area of parametric adaptive testing. With a single procedure, it is possible to implement a wide variety of experimental designs, including conventional threshold measurement; measurement of psychometric function parameters, such as slope and lapse; estimation of the contrast sensitivity function; measurement of increment threshold functions; measurement of noise-masking functions; Thurstone scale estimation using pair comparisons; and categorical ratings on linear and circular stimulus dimensions. QUEST+ provides a general method to accelerate data collection in many areas of cognitive and perceptual science.
THz-SAR Vibrating Target Imaging via the Bayesian Method
Directory of Open Access Journals (Sweden)
Bin Deng
2017-01-01
Full Text Available Target vibration bears important information for target recognition, and terahertz, due to significant micro-Doppler effects, has strong advantages for remotely sensing vibrations. In this paper, the imaging characteristics of vibrating targets with THz-SAR are at first analyzed. An improved algorithm based on an excellent Bayesian approach, that is, the expansion-compression variance-component (ExCoV method, has been proposed for reconstructing scattering coefficients of vibrating targets, which provides more robust and efficient initialization and overcomes the deficiencies of sidelobes as well as artifacts arising from the traditional correlation method. A real vibration measurement experiment of idle cars was performed to validate the range model. Simulated SAR data of vibrating targets and a tank model in a real background in 220 GHz show good performance at low SNR. Rapidly evolving high-power terahertz devices will offer viable THz-SAR application at a distance of several kilometers.
A variational Bayesian method to inverse problems with impulsive noise
Jin, Bangti
2012-01-01
We propose a novel numerical method for solving inverse problems subject to impulsive noises which possibly contain a large number of outliers. The approach is of Bayesian type, and it exploits a heavy-tailed t distribution for data noise to achieve robustness with respect to outliers. A hierarchical model with all hyper-parameters automatically determined from the given data is described. An algorithm of variational type by minimizing the Kullback-Leibler divergence between the true posteriori distribution and a separable approximation is developed. The numerical method is illustrated on several one- and two-dimensional linear and nonlinear inverse problems arising from heat conduction, including estimating boundary temperature, heat flux and heat transfer coefficient. The results show its robustness to outliers and the fast and steady convergence of the algorithm. © 2011 Elsevier Inc.
On Bayesian methods of exploring qualitative interactions for targeted treatment.
Chen, Wei; Ghosh, Debashis; Raghunathan, Trivellore E; Norkin, Maxim; Sargent, Daniel J; Bepler, Gerold
2012-12-10
Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.
Bayesian Analysis of Multiple Populations I: Statistical and Computational Methods
Stenning, D C; Robinson, E; van Dyk, D A; von Hippel, T; Sarajedini, A; Stein, N
2016-01-01
We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending earlier statistical models for open clusters composed of simple (single) stellar populations (vanDyk et al. 2009, Stein et al. 2013). Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties---age, metallicity, helium abundance, distance, absorption, and initial mass---are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two-population clusters, and al...
A Monte Carlo Evaluation of Maximum Likelihood Multidimensional Scaling Methods
Bijmolt, T.H.A.; Wedel, M.
1996-01-01
We compare three alternative Maximum Likelihood Multidimensional Scaling methods for pairwise dissimilarity ratings, namely MULTISCALE, MAXSCAL, and PROSCAL in a Monte Carlo study.The three MLMDS methods recover the true con gurations very well.The recovery of the true dimensionality depends on the
Insights on the Bayesian spectral density method for operational modal analysis
Au, Siu-Kui
2016-01-01
This paper presents a study on the Bayesian spectral density method for operational modal analysis. The method makes Bayesian inference of the modal properties by using the sample power spectral density (PSD) matrix averaged over independent sets of ambient data. In the typical case with a single set of data, it is divided into non-overlapping segments and they are assumed to be independent. This study is motivated by a recent paper that reveals a mathematical equivalence of the method with the Bayesian FFT method. The latter does not require averaging concepts or the independent segment assumption. This study shows that the equivalence does not hold in reality because the theoretical long data asymptotic distribution of the PSD matrix may not be valid. A single time history can be considered long for the Bayesian FFT method but not necessarily for the Bayesian PSD method, depending on the number of segments.
Bayesian signal processing classical, modern, and particle filtering methods
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 Maximum Entropy Method for a Robust Portfolio Problem
Directory of Open Access Journals (Sweden)
Yingying Xu
2014-06-01
Full Text Available We propose a continuous maximum entropy method to investigate the robustoptimal portfolio selection problem for the market with transaction costs and dividends.This robust model aims to maximize the worst-case portfolio return in the case that allof asset returns lie within some prescribed intervals. A numerical optimal solution tothe problem is obtained by using a continuous maximum entropy method. Furthermore,some numerical experiments indicate that the robust model in this paper can result in betterportfolio performance than a classical mean-variance model.
Computer-aided diagnosis system: a Bayesian hybrid classification method.
Calle-Alonso, F; Pérez, C J; Arias-Nicolás, J P; Martín, J
2013-10-01
A novel method to classify multi-class biomedical objects is presented. The method is based on a hybrid approach which combines pairwise comparison, Bayesian regression and the k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance feedback framework. In the latter case, the information obtained from both an expert and the automatic classification is iteratively used to improve the results until a certain accuracy level is achieved, then, the learning process is finished and new classifications can be automatically performed. The method has been applied in two biomedical contexts by following the same cross-validation schemes as in the original studies. The first one refers to cancer diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second one considers the diagnosis of pathologies of the vertebral column. The original method achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different cross-validation schemes. Even with no supervision, the proposed method reaches 96.71% and 97.32% in these two cases. By using a supervised framework the achieved accuracy is 97.74%. Furthermore, all abnormal cases were correctly classified.
Trends in epidemiology in the 21st century: time to adopt Bayesian methods
Directory of Open Access Journals (Sweden)
Edson Zangiacomi Martinez
2014-04-01
Full Text Available 2013 marked the 250th anniversary of the presentation of Bayes’ theorem by the philosopher Richard Price. Thomas Bayes was a figure little known in his own time, but in the 20th century the theorem that bears his name became widely used in many fields of research. The Bayes theorem is the basis of the so-called Bayesian methods, an approach to statistical inference that allows studies to incorporate prior knowledge about relevant data characteristics into statistical analysis. Nowadays, Bayesian methods are widely used in many different areas such as astronomy, economics, marketing, genetics, bioinformatics and social sciences. This study observed that a number of authors discussed recent advances in techniques and the advantages of Bayesian methods for the analysis of epidemiological data. This article presents an overview of Bayesian methods, their application to epidemiological research and the main areas of epidemiology which should benefit from the use of Bayesian methods in coming years.
A Study of New Method for Weapon System Effectiveness Evaluation Based on Bayesian Network
Institute of Scientific and Technical Information of China (English)
YAN Dai-wei; GU Liang-xian; PAN Lei
2008-01-01
As weapon system effectiveness is affected by many factors, its evaluation is essentially a multi-criterion decision making problem for its complexity. The evaluation model of the effectiveness is established on the basis of metrics architecture of the effectiveness. The Bayesian network, which is used to evaluate the effectiveness, is established based on the metrics architecture and the evaluation models. For getting the weights of the metrics by Bayesian network, subjective initial values of the weights are given, gradient ascent algorithm is adopted, and the reasonable values of the weights are achieved. And then the effectiveness of every weapon system project is gained. The weapon system, whose effectiveness is relative maximum, is the optimization system. The research result shows that this method can solve the problem of AHP method which evaluation results are not compatible to the practice results and overcome the shortcoming of neural network in multilayer and multi-criterion decision. The method offers a new approaeh for evaluating the effectiveness.
Time series analysis by the Maximum Entropy method
Energy Technology Data Exchange (ETDEWEB)
Kirk, B.L.; Rust, B.W.; Van Winkle, W.
1979-01-01
The principal subject of this report is the use of the Maximum Entropy method for spectral analysis of time series. The classical Fourier method is also discussed, mainly as a standard for comparison with the Maximum Entropy method. Examples are given which clearly demonstrate the superiority of the latter method over the former when the time series is short. The report also includes a chapter outlining the theory of the method, a discussion of the effects of noise in the data, a chapter on significance tests, a discussion of the problem of choosing the prediction filter length, and, most importantly, a description of a package of FORTRAN subroutines for making the various calculations. Cross-referenced program listings are given in the appendices. The report also includes a chapter demonstrating the use of the programs by means of an example. Real time series like the lynx data and sunspot numbers are also analyzed. 22 figures, 21 tables, 53 references.
Bayesian Method with Spatial Constraint for Retinal Vessel Segmentation
Directory of Open Access Journals (Sweden)
Zhiyong Xiao
2013-01-01
Full Text Available A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can identify blood vessels in the retinal image. Evaluation of the developed method is done on real retinal images which are from the DRIVE database and the STARE database. The performance is analyzed and compared to other published methods using a number of measures which include accuracy, sensitivity, and specificity. The proposed approach is proved to be effective on these two databases. The average accuracy, sensitivity, and specificity on the DRIVE database are 0.9529, 0.7513, and 0.9792, respectively, and for the STARE database 0.9476, 0.7147, and 0.9735, respectively. The performance is better than that of other vessel segmentation methods.
Maximum super angle optimization method for array antenna pattern synthesis
DEFF Research Database (Denmark)
Wu, Ji; Roederer, A. G
1991-01-01
Different optimization criteria related to antenna pattern synthesis are discussed. Based on the maximum criteria and vector space representation, a simple and efficient optimization method is presented for array and array fed reflector power pattern synthesis. A sector pattern synthesized by a 20...
Novel TPPO Based Maximum Power Point Method for Photovoltaic System
Directory of Open Access Journals (Sweden)
ABBASI, M. A.
2017-08-01
Full Text Available Photovoltaic (PV system has a great potential and it is installed more when compared with other renewable energy sources nowadays. However, the PV system cannot perform optimally due to its solid reliance on climate conditions. Due to this dependency, PV system does not operate at its maximum power point (MPP. Many MPP tracking methods have been proposed for this purpose. One of these is the Perturb and Observe Method (P&O which is the most famous due to its simplicity, less cost and fast track. But it deviates from MPP in continuously changing weather conditions, especially in rapidly changing irradiance conditions. A new Maximum Power Point Tracking (MPPT method, Tetra Point Perturb and Observe (TPPO, has been proposed to improve PV system performance in changing irradiance conditions and the effects on characteristic curves of PV array module due to varying irradiance are delineated. The Proposed MPPT method has shown better results in increasing the efficiency of a PV system.
Propane spectral resolution enhancement by the maximum entropy method
Bonavito, N. L.; Stewart, K. P.; Hurley, E. J.; Yeh, K. C.; Inguva, R.
1990-01-01
The Burg algorithm for maximum entropy power spectral density estimation is applied to a time series of data obtained from a Michelson interferometer and compared with a standard FFT estimate for resolution capability. The propane transmittance spectrum was estimated by use of the FFT with a 2 to the 18th data sample interferogram, giving a maximum unapodized resolution of 0.06/cm. This estimate was then interpolated by zero filling an additional 2 to the 18th points, and the final resolution was taken to be 0.06/cm. Comparison of the maximum entropy method (MEM) estimate with the FFT was made over a 45/cm region of the spectrum for several increasing record lengths of interferogram data beginning at 2 to the 10th. It is found that over this region the MEM estimate with 2 to the 16th data samples is in close agreement with the FFT estimate using 2 to the 18th samples.
Optical and terahertz spectra analysis by the maximum entropy method.
Vartiainen, Erik M; Peiponen, Kai-Erik
2013-06-01
Phase retrieval is one of the classical problems in various fields of physics including x-ray crystallography, astronomy and spectroscopy. It arises when only an amplitude measurement on electric field can be made while both amplitude and phase of the field are needed for obtaining the desired material properties. In optical and terahertz spectroscopies, in particular, phase retrieval is a one-dimensional problem, which is considered as unsolvable in general. Nevertheless, an approach utilizing the maximum entropy principle has proven to be a feasible tool in various applications of optical, both linear and nonlinear, as well as in terahertz spectroscopies, where the one-dimensional phase retrieval problem arises. In this review, we focus on phase retrieval using the maximum entropy method in various spectroscopic applications. We review the theory behind the method and illustrate through examples why and how the method works, as well as discuss its limitations.
An improved maximum power point tracking method for photovoltaic systems
Energy Technology Data Exchange (ETDEWEB)
Tafticht, T.; Agbossou, K.; Doumbia, M.L.; Cheriti, A. [Institut de recherche sur l' hydrogene, Departement de genie electrique et genie informatique, Universite du Quebec a Trois-Rivieres, C.P. 500, Trois-Rivieres (QC) (Canada)
2008-07-15
In most of the maximum power point tracking (MPPT) methods described currently in the literature, the optimal operation point of the photovoltaic (PV) systems is estimated by linear approximations. However these approximations can lead to less than optimal operating conditions and hence reduce considerably the performances of the PV system. This paper proposes a new approach to determine the maximum power point (MPP) based on measurements of the open-circuit voltage of the PV modules, and a nonlinear expression for the optimal operating voltage is developed based on this open-circuit voltage. The approach is thus a combination of the nonlinear and perturbation and observation (P and O) methods. The experimental results show that the approach improves clearly the tracking efficiency of the maximum power available at the output of the PV modules. The new method reduces the oscillations around the MPP, and increases the average efficiency of the MPPT obtained. The new MPPT method will deliver more power to any generic load or energy storage media. (author)
Improving predictability of time series using maximum entropy methods
Chliamovitch, G.; Dupuis, A.; Golub, A.; Chopard, B.
2015-04-01
We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is more efficient than sampling, in the sense that shorter historical samples have to be considered to reach the same accuracy. Considering short samples is of particular interest when modelling smoothly non-stationary processes, which provides, under some conditions, a powerful forecasting tool. The method is illustrated for a discretized empirical series of exchange rates.
CEO emotional bias and investment decision, Bayesian network method
Directory of Open Access Journals (Sweden)
Jarboui Anis
2012-08-01
Full Text Available This research examines the determinants of firms’ investment introducing a behavioral perspective that has received little attention in corporate finance literature. The following central hypothesis emerges from a set of recently developed theories: Investment decisions are influenced not only by their fundamentals but also depend on some other factors. One factor is the biasness of any CEO to their investment, biasness depends on the cognition and emotions, because some leaders use them as heuristic for the investment decision instead of fundamentals. This paper shows how CEO emotional bias (optimism, loss aversion and overconfidence affects the investment decisions. The proposed model of this paper uses Bayesian Network Method to examine this relationship. Emotional bias has been measured by means of a questionnaire comprising several items. As for the selected sample, it has been composed of some 100 Tunisian executives. Our results have revealed that the behavioral analysis of investment decision implies leader affected by behavioral biases (optimism, loss aversion, and overconfidence adjusts its investment choices based on their ability to assess alternatives (optimism and overconfidence and risk perception (loss aversion to create of shareholder value and ensure its place at the head of the management team.
CEO emotional bias and dividend policy: Bayesian network method
Directory of Open Access Journals (Sweden)
Azouzi Mohamed Ali
2012-10-01
Full Text Available This paper assumes that managers, investors, or both behave irrationally. In addition, even though scholars have investigated behavioral irrationality from three angles, investor sentiment, investor biases and managerial biases, we focus on the relationship between one of the managerial biases, overconfidence and dividend policy. Previous research investigating the relationship between overconfidence and financial decisions has studied investment, financing decisions and firm values. However, there are only a few exceptions to examine how a managerial emotional bias (optimism, loss aversion and overconfidence affects dividend policies. This stream of research contends whether to distribute dividends or not depends on how managers perceive of the company’s future. I will use Bayesian network method to examine this relation. Emotional bias has been measured by means of a questionnaire comprising several items. As for the selected sample, it has been composed of some100 Tunisian executives. Our results have revealed that leader affected by behavioral biases (optimism, loss aversion, and overconfidence adjusts its dividend policy choices based on their ability to assess alternatives (optimism and overconfidence and risk perception (loss aversion to create of shareholder value and ensure its place at the head of the management team.
Bayesian Method of Moments (BMOM) Analysis of Mean and Regression Models
Zellner, Arnold
2008-01-01
A Bayesian method of moments/instrumental variable (BMOM/IV) approach is developed and applied in the analysis of the important mean and multiple regression models. Given a single set of data, it is shown how to obtain posterior and predictive moments without the use of likelihood functions, prior densities and Bayes' Theorem. The posterior and predictive moments, based on a few relatively weak assumptions, are then used to obtain maximum entropy densities for parameters, realized error terms and future values of variables. Posterior means for parameters and realized error terms are shown to be equal to certain well known estimates and rationalized in terms of quadratic loss functions. Conditional maxent posterior densities for means and regression coefficients given scale parameters are in the normal form while scale parameters' maxent densities are in the exponential form. Marginal densities for individual regression coefficients, realized error terms and future values are in the Laplace or double-exponenti...
The Maximum Patch Method for Directional Dark Matter Detection
Henderson, Shawn; Fisher, Peter
2008-01-01
Present and planned dark matter detection experiments search for WIMP-induced nuclear recoils in poorly known background conditions. In this environment, the maximum gap statistical method provides a way of setting more sensitive cross section upper limits by incorporating known signal information. We give a recipe for the numerical calculation of upper limits for planned directional dark matter detection experiments, that will measure both recoil energy and angle, based on the gaps between events in two-dimensional phase space.
Multifrequency Bayesian compressive sensing methods for microwave imaging.
Poli, Lorenzo; Oliveri, Giacomo; Ding, Ping Ping; Moriyama, Toshifumi; Massa, Andrea
2014-11-01
The Bayesian retrieval of sparse scatterers under multifrequency transverse magnetic illuminations is addressed. Two innovative imaging strategies are formulated to process the spectral content of microwave scattering data according to either a frequency-hopping multistep scheme or a multifrequency one-shot scheme. To solve the associated inverse problems, customized implementations of single-task and multitask Bayesian compressive sensing are introduced. A set of representative numerical results is discussed to assess the effectiveness and the robustness against the noise of the proposed techniques also in comparison with some state-of-the-art deterministic strategies.
Maximum likelihood method and Fisher's information in physics and econophysics
Syska, Jacek
2012-01-01
Three steps in the development of the maximum likelihood (ML) method are presented. At first, the application of the ML method and Fisher information notion in the model selection analysis is described (Chapter 1). The fundamentals of differential geometry in the construction of the statistical space are introduced, illustrated also by examples of the estimation of the exponential models. At second, the notions of the relative entropy and the information channel capacity are introduced (Chapter 2). The observed and expected structural information principle (IP) and the variational IP of the modified extremal physical information (EPI) method of Frieden and Soffer are presented and discussed (Chapter 3). The derivation of the structural IP based on the analyticity of the logarithm of the likelihood function and on the metricity of the statistical space of the system is given. At third, the use of the EPI method is developed (Chapters 4-5). The information channel capacity is used for the field theory models cl...
Influence of Pareto optimality on the maximum entropy methods
Peddavarapu, Sreehari; Sunil, Gujjalapudi Venkata Sai; Raghuraman, S.
2017-07-01
Galerkin meshfree schemes are emerging as a viable substitute to finite element method to solve partial differential equations for the large deformations as well as crack propagation problems. However, the introduction of Shanon-Jayne's entropy principle in to the scattered data approximation has deviated from the trend of defining the approximation functions, resulting in maximum entropy approximants. Further in addition to this, an objective functional which controls the degree of locality resulted in Local maximum entropy approximants. These are based on information-theoretical Pareto optimality between entropy and degree of locality that are defining the basis functions to the scattered nodes. The degree of locality in turn relies on the choice of locality parameter and prior (weight) function. The proper choices of both plays vital role in attain the desired accuracy. Present work is focused on the choice of locality parameter which defines the degree of locality and priors: Gaussian, Cubic spline and quartic spline functions on the behavior of local maximum entropy approximants.
McGuire, Jimmy A; Witt, Christopher C; Altshuler, Douglas L; Remsen, J V
2007-10-01
Hummingbirds are an important model system in avian biology, but to date the group has been the subject of remarkably few phylogenetic investigations. Here we present partitioned Bayesian and maximum likelihood phylogenetic analyses for 151 of approximately 330 species of hummingbirds and 12 outgroup taxa based on two protein-coding mitochondrial genes (ND2 and ND4), flanking tRNAs, and two nuclear introns (AK1 and BFib). We analyzed these data under several partitioning strategies ranging between unpartitioned and a maximum of nine partitions. In order to select a statistically justified partitioning strategy following partitioned Bayesian analysis, we considered four alternative criteria including Bayes factors, modified versions of the Akaike information criterion for small sample sizes (AIC(c)), Bayesian information criterion (BIC), and a decision-theoretic methodology (DT). Following partitioned maximum likelihood analyses, we selected a best-fitting strategy using hierarchical likelihood ratio tests (hLRTS), the conventional AICc, BIC, and DT, concluding that the most stringent criterion, the performance-based DT, was the most appropriate methodology for selecting amongst partitioning strategies. In the context of our well-resolved and well-supported phylogenetic estimate, we consider the historical biogeography of hummingbirds using ancestral state reconstructions of (1) primary geographic region of occurrence (i.e., South America, Central America, North America, Greater Antilles, Lesser Antilles), (2) Andean or non-Andean geographic distribution, and (3) minimum elevational occurrence. These analyses indicate that the basal hummingbird assemblages originated in the lowlands of South America, that most of the principle clades of hummingbirds (all but Mountain Gems and possibly Bees) originated on this continent, and that there have been many (at least 30) independent invasions of other primary landmasses, especially Central America.
Implementation of the Maximum Entropy Method for Analytic Continuation
Levy, Ryan; Gull, Emanuel
2016-01-01
We present $\\texttt{Maxent}$, a tool for performing analytic continuation of spectral functions using the maximum entropy method. The code operates on discrete imaginary axis datasets (values with uncertainties) and transforms this input to the real axis. The code works for imaginary time and Matsubara frequency data and implements the 'Legendre' representation of finite temperature Green's functions. It implements a variety of kernels, default models, and grids for continuing bosonic, fermionic, anomalous, and other data. Our implementation is licensed under GPLv2 and extensively documented. This paper shows the use of the programs in detail.
Implementation of the maximum entropy method for analytic continuation
Levy, Ryan; LeBlanc, J. P. F.; Gull, Emanuel
2017-06-01
We present Maxent, a tool for performing analytic continuation of spectral functions using the maximum entropy method. The code operates on discrete imaginary axis datasets (values with uncertainties) and transforms this input to the real axis. The code works for imaginary time and Matsubara frequency data and implements the 'Legendre' representation of finite temperature Green's functions. It implements a variety of kernels, default models, and grids for continuing bosonic, fermionic, anomalous, and other data. Our implementation is licensed under GPLv3 and extensively documented. This paper shows the use of the programs in detail.
A Maximum-Entropy Method for Estimating the Spectrum
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Based on the maximum-entropy (ME) principle, a new power spectral estimator for random waves is derived in the form of ~S(ω)=(a/8)-H2(2π)d+1ω-(d+2)exp[-b(2π/ω)n], by solving a variational problem subject to some quite general constraints. This robust method is comprehensive enough to describe the wave spectra even in extreme wave conditions and is superior to periodogram method that is not suitable to process comparatively short or intensively unsteady signals for its tremendous boundary effect and some inherent defects of FFT. Fortunately, the newly derived method for spectral estimation works fairly well, even though the sample data sets are very short and unsteady, and the reliability and efficiency of this spectral estimator have been preliminarily proved.
Evaluating maximum likelihood estimation methods to determine the hurst coefficients
Kendziorski, C. M.; Bassingthwaighte, J. B.; Tonellato, P. J.
1999-12-01
A maximum likelihood estimation method implemented in S-PLUS ( S-MLE) to estimate the Hurst coefficient ( H) is evaluated. The Hurst coefficient, with 0.5long memory time series by quantifying the rate of decay of the autocorrelation function. S-MLE was developed to estimate H for fractionally differenced (fd) processes. However, in practice it is difficult to distinguish between fd processes and fractional Gaussian noise (fGn) processes. Thus, the method is evaluated for estimating H for both fd and fGn processes. S-MLE gave biased results of H for fGn processes of any length and for fd processes of lengths less than 2 10. A modified method is proposed to correct for this bias. It gives reliable estimates of H for both fd and fGn processes of length greater than or equal to 2 11.
parallelMCMCcombine: an R package for bayesian methods for big data and analytics.
Directory of Open Access Journals (Sweden)
Alexey Miroshnikov
Full Text Available Recent advances in big data and analytics research have provided a wealth of large data sets that are too big to be analyzed in their entirety, due to restrictions on computer memory or storage size. New Bayesian methods have been developed for data sets that are large only due to large sample sizes. These methods partition big data sets into subsets and perform independent Bayesian Markov chain Monte Carlo analyses on the subsets. The methods then combine the independent subset posterior samples to estimate a posterior density given the full data set. These approaches were shown to be effective for Bayesian models including logistic regression models, Gaussian mixture models and hierarchical models. Here, we introduce the R package parallelMCMCcombine which carries out four of these techniques for combining independent subset posterior samples. We illustrate each of the methods using a Bayesian logistic regression model for simulation data and a Bayesian Gamma model for real data; we also demonstrate features and capabilities of the R package. The package assumes the user has carried out the Bayesian analysis and has produced the independent subposterior samples outside of the package. The methods are primarily suited to models with unknown parameters of fixed dimension that exist in continuous parameter spaces. We envision this tool will allow researchers to explore the various methods for their specific applications and will assist future progress in this rapidly developing field.
parallelMCMCcombine: an R package for bayesian methods for big data and analytics.
Miroshnikov, Alexey; Conlon, Erin M
2014-01-01
Recent advances in big data and analytics research have provided a wealth of large data sets that are too big to be analyzed in their entirety, due to restrictions on computer memory or storage size. New Bayesian methods have been developed for data sets that are large only due to large sample sizes. These methods partition big data sets into subsets and perform independent Bayesian Markov chain Monte Carlo analyses on the subsets. The methods then combine the independent subset posterior samples to estimate a posterior density given the full data set. These approaches were shown to be effective for Bayesian models including logistic regression models, Gaussian mixture models and hierarchical models. Here, we introduce the R package parallelMCMCcombine which carries out four of these techniques for combining independent subset posterior samples. We illustrate each of the methods using a Bayesian logistic regression model for simulation data and a Bayesian Gamma model for real data; we also demonstrate features and capabilities of the R package. The package assumes the user has carried out the Bayesian analysis and has produced the independent subposterior samples outside of the package. The methods are primarily suited to models with unknown parameters of fixed dimension that exist in continuous parameter spaces. We envision this tool will allow researchers to explore the various methods for their specific applications and will assist future progress in this rapidly developing field.
Application of the maximum entropy method to profile analysis
Energy Technology Data Exchange (ETDEWEB)
Armstrong, N.; Kalceff, W. [University of Technology, Department of Applied Physics, Sydney, NSW (Australia); Cline, J.P. [National Institute of Standards and Technology, Gaithersburg, (United States)
1999-12-01
Full text: A maximum entropy (MaxEnt) method for analysing crystallite size- and strain-induced x-ray profile broadening is presented. This method treats the problems of determining the specimen profile, crystallite size distribution, and strain distribution in a general way by considering them as inverse problems. A common difficulty faced by many experimenters is their inability to determine a well-conditioned solution of the integral equation, which preserves the positivity of the profile or distribution. We show that the MaxEnt method overcomes this problem, while also enabling a priori information, in the form of a model, to be introduced into it. Additionally, we demonstrate that the method is fully quantitative, in that uncertainties in the solution profile or solution distribution can be determined and used in subsequent calculations, including mean particle sizes and rms strain. An outline of the MaxEnt method is presented for the specific problems of determining the specimen profile and crystallite or strain distributions for the correspondingly broadened profiles. This approach offers an alternative to standard methods such as those of Williamson-Hall and Warren-Averbach. An application of the MaxEnt method is demonstrated in the analysis of alumina size-broadened diffraction data (from NIST, Gaithersburg). It is used to determine the specimen profile and column-length distribution of the scattering domains. Finally, these results are compared with the corresponding Williamson-Hall and Warren-Averbach analyses. Copyright (1999) Australian X-ray Analytical Association Inc.
Bayesian Population Forecasting: Extending the Lee-Carter Method.
Wiśniowski, Arkadiusz; Smith, Peter W F; Bijak, Jakub; Raymer, James; Forster, Jonathan J
2015-06-01
In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.
Bayesian methods for the analysis of inequality constrained contingency tables.
Laudy, Olav; Hoijtink, Herbert
2007-04-01
A Bayesian methodology for the analysis of inequality constrained models for contingency tables is presented. The problem of interest lies in obtaining the estimates of functions of cell probabilities subject to inequality constraints, testing hypotheses and selection of the best model. Constraints on conditional cell probabilities and on local, global, continuation and cumulative odds ratios are discussed. A Gibbs sampler to obtain a discrete representation of the posterior distribution of the inequality constrained parameters is used. Using this discrete representation, the credibility regions of functions of cell probabilities can be constructed. Posterior model probabilities are used for model selection and hypotheses are tested using posterior predictive checks. The Bayesian methodology proposed is illustrated in two examples.
Understanding data better with Bayesian and global statistical methods
Press, W H
1996-01-01
To understand their data better, astronomers need to use statistical tools that are more advanced than traditional ``freshman lab'' statistics. As an illustration, the problem of combining apparently incompatible measurements of a quantity is presented from both the traditional, and a more sophisticated Bayesian, perspective. Explicit formulas are given for both treatments. Results are shown for the value of the Hubble Constant, and a 95% confidence interval of 66 < H0 < 82 (km/s/Mpc) is obtained.
Gradient-based stochastic optimization methods in Bayesian experimental design
2012-01-01
Optimal experimental design (OED) seeks experiments expected to yield the most useful data for some purpose. In practical circumstances where experiments are time-consuming or resource-intensive, OED can yield enormous savings. We pursue OED for nonlinear systems from a Bayesian perspective, with the goal of choosing experiments that are optimal for parameter inference. Our objective in this context is the expected information gain in model parameters, which in general can only be estimated u...
Sparse Event Modeling with Hierarchical Bayesian Kernel Methods
2016-01-05
the kernel function which depends on the application and the model user. This research uses the most popular kernel function, the radial basis...an important role in the nation’s economy. Unfortunately, the system’s reliability is declining due to the aging components of the network [Grier...kernel function. Gaussian Bayesian kernel models became very popular recently and were extended and applied to a number of classification problems. An
Ball, R D
2001-11-01
We describe an approximate method for the analysis of quantitative trait loci (QTL) based on model selection from multiple regression models with trait values regressed on marker genotypes, using a modification of the easily calculated Bayesian information criterion to estimate the posterior probability of models with various subsets of markers as variables. The BIC-delta criterion, with the parameter delta increasing the penalty for additional variables in a model, is further modified to incorporate prior information, and missing values are handled by multiple imputation. Marginal probabilities for model sizes are calculated, and the posterior probability of nonzero model size is interpreted as the posterior probability of existence of a QTL linked to one or more markers. The method is demonstrated on analysis of associations between wood density and markers on two linkage groups in Pinus radiata. Selection bias, which is the bias that results from using the same data to both select the variables in a model and estimate the coefficients, is shown to be a problem for commonly used non-Bayesian methods for QTL mapping, which do not average over alternative possible models that are consistent with the data.
Test images for the maximum entropy image restoration method
Mackey, James E.
1990-01-01
One of the major activities of any experimentalist is data analysis and reduction. In solar physics, remote observations are made of the sun in a variety of wavelengths and circumstances. In no case is the data collected free from the influence of the design and operation of the data gathering instrument as well as the ever present problem of noise. The presence of significant noise invalidates the simple inversion procedure regardless of the range of known correlation functions. The Maximum Entropy Method (MEM) attempts to perform this inversion by making minimal assumptions about the data. To provide a means of testing the MEM and characterizing its sensitivity to noise, choice of point spread function, type of data, etc., one would like to have test images of known characteristics that can represent the type of data being analyzed. A means of reconstructing these images is presented.
A probabilistic crack size quantification method using in-situ Lamb wave test and Bayesian updating
Yang, Jinsong; He, Jingjing; Guan, Xuefei; Wang, Dengjiang; Chen, Huipeng; Zhang, Weifang; Liu, Yongming
2016-10-01
This paper presents a new crack size quantification method based on in-situ Lamb wave testing and Bayesian method. The proposed method uses coupon test to develop a baseline quantification model between the crack size and damage sensitive features. In-situ Lamb wave testing data on actual structures are used to update the baseline model parameters using Bayesian method to achieve more accurate crack size predictions. To demonstrate the proposed method, Lamb wave testing on simple plates with artificial cracks of different sizes is performed using surface-bonded piezoelectric wafers, and the data are used to obtain the baseline model. Two damage sensitive features, namely, the phase change and normalized amplitude are identified using signal processing techniques and used in the model. To validate the effectiveness of the method, the damage data from an in-situ fatigue testing on a realistic lap-joint component are used to update the baseline model using Bayesian method.
A Clustering Method Based on the Maximum Entropy Principle
Directory of Open Access Journals (Sweden)
Edwin Aldana-Bobadilla
2015-01-01
Full Text Available Clustering is an unsupervised process to determine which unlabeled objects in a set share interesting properties. The objects are grouped into k subsets (clusters whose elements optimize a proximity measure. Methods based on information theory have proven to be feasible alternatives. They are based on the assumption that a cluster is one subset with the minimal possible degree of “disorder”. They attempt to minimize the entropy of each cluster. We propose a clustering method based on the maximum entropy principle. Such a method explores the space of all possible probability distributions of the data to find one that maximizes the entropy subject to extra conditions based on prior information about the clusters. The prior information is based on the assumption that the elements of a cluster are “similar” to each other in accordance with some statistical measure. As a consequence of such a principle, those distributions of high entropy that satisfy the conditions are favored over others. Searching the space to find the optimal distribution of object in the clusters represents a hard combinatorial problem, which disallows the use of traditional optimization techniques. Genetic algorithms are a good alternative to solve this problem. We benchmark our method relative to the best theoretical performance, which is given by the Bayes classifier when data are normally distributed, and a multilayer perceptron network, which offers the best practical performance when data are not normal. In general, a supervised classification method will outperform a non-supervised one, since, in the first case, the elements of the classes are known a priori. In what follows, we show that our method’s effectiveness is comparable to a supervised one. This clearly exhibits the superiority of our method.
Ferragina, A; de los Campos, G; Vazquez, A I; Cecchinato, A; Bittante, G
2015-11-01
calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R(2) value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R(2) (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R(2) of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations.
Bayesian network modeling method based on case reasoning for emergency decision-making
Directory of Open Access Journals (Sweden)
XU Lei
2013-06-01
Full Text Available Bayesian network has the abilities of probability expression, uncertainty management and multi-information fusion.It can support emergency decision-making, which can improve the efficiency of decision-making.Emergency decision-making is highly time sensitive, which requires shortening the Bayesian Network modeling time as far as possible.Traditional Bayesian network modeling methods are clearly unable to meet that requirement.Thus, a Bayesian network modeling method based on case reasoning for emergency decision-making is proposed.The method can obtain optional cases through case matching by the functions of similarity degree and deviation degree.Then,new Bayesian network can be built through case adjustment by case merging and pruning.An example is presented to illustrate and test the proposed method.The result shows that the method does not have a huge search space or need sample data.The only requirement is the collection of expert knowledge and historical case models.Compared with traditional methods, the proposed method can reuse historical case models, which can reduce the modeling time and improve the efficiency.
O'Neill, Philip D
2002-01-01
Recent Bayesian methods for the analysis of infectious disease outbreak data using stochastic epidemic models are reviewed. These methods rely on Markov chain Monte Carlo methods. Both temporal and non-temporal data are considered. The methods are illustrated with a number of examples featuring different models and datasets.
Continuous maximum flow segmentation method for nanoparticle interaction analysis.
Marak, L; Tankyevych, O; Talbot, H
2011-10-01
In recent years, tomographic three-dimensional reconstruction approaches using electrons rather than X-rays have become popular. Such images produced with a transmission electron microscope make it possible to image nanometre-scale materials in three-dimensional. However, they are also noisy, limited in contrast and most often have a very poor resolution along the axis of the electron beam. The analysis of images stemming from such modalities, whether fully or semiautomated, is therefore more complicated. In particular, segmentation of objects is difficult. In this paper, we propose to use the continuous maximum flow segmentation method based on a globally optimal minimal surface model. The use of this fully automated segmentation and filtering procedure is illustrated on two different nanoparticle samples and provide comparisons with other classical segmentation methods. The main objectives are the measurement of the attraction rate of polystyrene beads to silica nanoparticle (for the first sample) and interaction of silica nanoparticles with large unilamellar liposomes (for the second sample). We also illustrate how precise measurements such as contact angles can be performed.
Analyzing bioassay data using Bayesian methods -- A primer
Energy Technology Data Exchange (ETDEWEB)
Miller, G.; Inkret, W.C.; Schillaci, M.E.
1997-10-16
The classical statistics approach used in health physics for the interpretation of measurements is deficient in that it does not allow for the consideration of needle in a haystack effects, where events that are rare in a population are being detected. In fact, this is often the case in health physics measurements, and the false positive fraction is often very large using the prescriptions of classical statistics. Bayesian statistics provides an objective methodology to ensure acceptably small false positive fractions. The authors present the basic methodology and a heuristic discussion. Examples are given using numerically generated and real bioassay data (Tritium). Various analytical models are used to fit the prior probability distribution, in order to test the sensitivity to choice of model. Parametric studies show that the normalized Bayesian decision level k{sub {alpha}}-L{sub c}/{sigma}{sub 0}, where {sigma}{sub 0} is the measurement uncertainty for zero true amount, is usually in the range from 3 to 5 depending on the true positive rate. Four times {sigma}{sub 0} rather than approximately two times {sigma}{sub 0}, as in classical statistics, would often seem a better choice for the decision level.
Improved Maximum Entropy Method with an Extended Search Space
Rothkopf, Alexander
2012-01-01
We report on an improvement to the implementation of the Maximum Entropy Method (MEM). It amounts to departing from the search space obtained through a singular value decomposition (SVD) of the Kernel. Based on the shape of the SVD basis functions we argue that the MEM spectrum for given $N_\\tau$ data-points $D(\\tau)$ and prior information $m(\\omega)$ does not in general lie in this $N_\\tau$ dimensional singular subspace. Systematically extending the search basis will eventually recover the full search space and the correct extremum. We illustrate this idea through a mock data analysis inspired by actual lattice spectra, to show where our improvement becomes essential for the success of the MEM. To remedy the shortcomings of Bryan's SVD prescription we propose to use the real Fourier basis, which consists of trigonometric functions. Not only does our approach lead to more stable numerical behavior, as the SVD is not required for the determination of the basis functions, but also the resolution of the MEM beco...
Crandell, Jamie L.; Voils, Corrine I.; Chang, YunKyung; Sandelowski, Margarete
2010-01-01
The possible utility of Bayesian methods for the synthesis of qualitative and quantitative research has been repeatedly suggested but insufficiently investigated. In this project, we developed and used a Bayesian method for synthesis, with the goal of identifying factors that influence adherence to HIV medication regimens. We investigated the effect of 10 factors on adherence. Recognizing that not all factors were examined in all studies, we considered standard methods for dealing with missing data and chose a Bayesian data augmentation method. We were able to summarize, rank, and compare the effects of each of the 10 factors on medication adherence. This is a promising methodological development in the synthesis of qualitative and quantitative research. PMID:21572970
Bayesian methods for the conformational classification of eight-membered rings
DEFF Research Database (Denmark)
Pérez, J.; Nolsøe, Kim; Kessler, M.;
2005-01-01
Two methods for the classification of eight-membered rings based on a Bayesian analysis are presented. The two methods share the same probabilistic model for the measurement of torsion angles, but while the first method uses the canonical forms of cyclooctane and, given an empirical sequence of e...
Joint maximum likelihood and Bayesian channel estimation%联合最大似然贝叶斯信道估计
Institute of Scientific and Technical Information of China (English)
沈壁川; 郑建宏; 申敏
2008-01-01
在高信噪比情况下统计贝叶斯估计是一种有效的信道估计方法,但是在低信噪比时由于噪声估计不准确,其性能逐渐下降.研究了基于鲁棒的非线性降噪方法,提出了一个简化的联合最大似然贝叶斯信道估计.计算机仿真结果和分析表明这种方法在较高和较低的信噪比情况下,提高了信道估计和联合检测的性能.%Statistical Bayesian channel estimation is effective in suppressing noise floor for high SNR, but its performance degrades due to less reliable noise estimation in low SNR region. Based on a robust nonlinear de-noising technique for small signal, a simplified joint maximum likelihood and Bayesian channel estimation is proposed and investigated. Simulation results are presented and analysis shows it is promising to improve channel estimation and joint detection performance for both low and high SNR situations.
CEO Emotional Intelligence and Firms’ Financial Policies. Bayesian Network Method
Directory of Open Access Journals (Sweden)
Mohamed Ali Azouzi
2014-03-01
Full Text Available The aim of this paper is to explore the determinants of firms’ financial policies according to the manager’s psychological characteristics. More specifically, it examines the links between emotional intelligence, decision biases and the effectiveness of firms’ financial policies. The article finds that the main cause of an organization’s problems is the CEO’s emotional intelligence level. We introduce an approach based on Bayesian network techniques with a series of semi-directive interviews. The research paper represents an original approach because it characterizes behavioral corporate policy choices in emerging markets. To the best of our knowledge, this is the first study in the Tunisian context to explore this area of research. Our results show that Tunisian leaders adjust their decisions (on investments and distributions to minimize the risk of loss of compensation or reputation. They opt for decisions that minimize agency costs, transaction costs, and cognitive costs.
Bayesian Regression and Neuro-Fuzzy Methods Reliability Assessment for Estimating Streamflow
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Yaseen A. Hamaamin
2016-07-01
Full Text Available Accurate and efficient estimation of streamflow in a watershed’s tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and cost-effective approach to water resources planning and management. Two data-driven methods, Bayesian regression and adaptive neuro-fuzzy inference system (ANFIS, were tested separately as a faster alternative to a calibrated and validated Soil and Water Assessment Tool (SWAT model to predict streamflow in the Saginaw River Watershed of Michigan. For the data-driven modeling process, four structures were assumed and tested: general, temporal, spatial, and spatiotemporal. Results showed that both Bayesian regression and ANFIS can replicate global (watershed and local (subbasin results similar to a calibrated SWAT model. At the global level, Bayesian regression and ANFIS model performance were satisfactory based on Nash-Sutcliffe efficiencies of 0.99 and 0.97, respectively. At the subbasin level, Bayesian regression and ANFIS models were satisfactory for 155 and 151 subbasins out of 155 subbasins, respectively. Overall, the most accurate method was a spatiotemporal Bayesian regression model that outperformed other models at global and local scales. However, all ANFIS models performed satisfactory at both scales.
Energy Technology Data Exchange (ETDEWEB)
Katrinia M. Groth; Curtis L. Smith; Laura P. Swiler
2014-08-01
In the past several years, several international organizations have begun to collect data on human performance in nuclear power plant simulators. The data collected provide a valuable opportunity to improve human reliability analysis (HRA), but these improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this paper, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.
Safety assessment of infrastructures using a new Bayesian Monte Carlo method
Rajabalinejad, M.; Demirbilek, Z.
2011-01-01
A recently developed Bayesian Monte Carlo (BMC) method and its application to safety assessment of structures are described in this paper. We use a one-dimensional BMC method that was proposed in 2009 by Rajabalinejad in order to develop a weighted logical dependence between successive Monte Carlo s
Efficient Inversion in Underwater Acoustics with Analytic, Iterative and Sequential Bayesian Methods
2015-09-30
Iterative and Sequential Bayesian Methods Zoi-Heleni Michalopoulou Department of Mathematical Sciences New Jersey Institute of Technology...exploiting (fully or partially) the physics of the propagation medium. Algorithms are designed for inversion via the extraction of features of the...statistical modeling. • Develop methods for passive localization and inversion of environmental parameters that select features of propagation that are
Dimitrakakis, C.; Filipe, J.; Fred, A.; Sharp, B.
2010-01-01
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most problems of interest, the optimal solution involves planning in an infinitely large tree. However, it is possible to ob
A Comparison of the β-Substitution Method and a Bayesian Method for Analyzing Left-Censored Data.
Huynh, Tran; Quick, Harrison; Ramachandran, Gurumurthy; Banerjee, Sudipto; Stenzel, Mark; Sandler, Dale P; Engel, Lawrence S; Kwok, Richard K; Blair, Aaron; Stewart, Patricia A
2016-01-01
Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X0.95) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian method's performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the measurement data. We suggest the use of Bayesian methods if the practitioner has the
Application of a data-mining method based on Bayesian networks to lesion-deficit analysis
Herskovits, Edward H.; Gerring, Joan P.
2003-01-01
Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.
A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks
Murphy, Finbarr; Sheehan, Barry; Mullins, Martin; Bouwmeester, Hans; Marvin, Hans J. P.; Bouzembrak, Yamine; Costa, Anna L.; Das, Rasel; Stone, Vicki; Tofail, Syed A. M.
2016-11-01
While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Here we demonstrate the use of Bayesian networks as a reliable tool for NM risk estimation. This tool is tractable, accessible and scalable. Most importantly, it captures a broad span of data types, from complete, high quality data sets through to data sets with missing data and/or values with a relatively high spread of probability distribution. The tool is able to learn iteratively in order to further refine forecasts as the quality of data available improves. We demonstrate how this risk measurement approach works on NMs with varying degrees of risk potential, namely, carbon nanotubes, silver and titanium dioxide. The results afford even non-experts an accurate picture of the occupational risk probabilities associated with these NMs and, in doing so, demonstrated how NM risk can be evaluated into a tractable, quantitative risk comparator.
Estimates of European emissions of methyl chloroform using a Bayesian inversion method
Directory of Open Access Journals (Sweden)
M. Maione
2014-03-01
Full Text Available Methyl chloroform (MCF is a man-made chlorinated solvent contributing to the destruction of stratospheric ozone and is controlled under the Montreal Protocol on Substances that Deplete the Ozone Layer. Long-term, high-frequency observations of MCF carried out at three European sites show a constant decline of the background mixing ratios of MCF. However, we observe persistent non-negligible mixing ratio enhancements of MCF in pollution episodes suggesting unexpectedly high ongoing emissions in Europe. In order to identify the source regions and to give an estimate of the magnitude of such emissions, we have used a Bayesian inversion method and a point source analysis, based on high-frequency long-term observations at the three European sites. The inversion identified south-eastern France (SEF as a region with enhanced MCF emissions. This estimate was confirmed by the point source analysis. We performed this analysis using an eleven-year data set, from January 2002 to December 2012. Overall emissions estimated for the European study domain decreased nearly exponentially from 1.1 Gg yr−1 in 2002 to 0.32 Gg yr−1 in 2012, of which the estimated emissions from the SEF region accounted for 0.49 Gg yr−1 in 2002 and 0.20 Gg yr−1 in 2012. The European estimates are a significant fraction of the total semi-hemisphere (30–90° N emissions, contributing a minimum of 9.8% in 2004 and a maximum of 33.7% in 2011, of which on average 50% are from the SEF region. On the global scale, the SEF region is thus responsible from a minimum of 2.6% (in 2003 to a maximum of 10.3% (in 2009 of the global MCF emissions.
Bayesian Methods for the Physical Sciences. Learning from Examples in Astronomy and Physics.
Andreon, Stefano; Weaver, Brian
2015-05-01
-to-collect events or objects are over-represented in samples and difficult-to-collect are under-represented if not missing altogether. In this chapter we show how to account for non-random data collection to infer the properties of the population from the studied sample. Chapter 8: In this chapter we introduce regression models, i.e., how to fit (regress) one, or more quantities, against each other through a functional relationship and estimate any unknown parameters that dictate this relationship. Questions of interest include: how to deal with samples affected by selection effects? How does a rich data structure influence the fitted parameters? And what about non-linear multiple-predictor fits, upper/lower limits, measurements errors of different amplitudes and an intrinsic variety in the studied populations or an extra source of variability? A number of examples illustrate how to answer these questions and how to predict the value of an unavailable quantity by exploiting the existence of a trend with another, available, quantity. Chapter 9: This chapter provides some advice on how the careful scientist should perform model checking and sensitivity analysis, i.e., how to answer the following questions: is the considered model at odds with the current available data (the fitted data), for example because it is over-simplified compared to some specific complexity pointed out by the data? Furthermore, are the data informative about the quantity being measured or are results sensibly dependent on details of the fitted model? And, finally, what about if assumptions are uncertain? A number of examples illustrate how to answer these questions. Chapter 10: This chapter compares the performance of Bayesian methods against simple, non-Bayesian alternatives, such as maximum likelihood, minimal chi square, ordinary and weighted least square, bivariate correlated errors and intrinsic scatter, and robust estimates of location and scale. Performances are evaluated in terms of quality of the
Bayesian method for system reliability assessment of overlapping pass/fail data
Institute of Scientific and Technical Information of China (English)
Zhipeng Hao; Shengkui Zeng; Jianbin Guo
2015-01-01
For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing, the Bayesian analysis can improve the precision of the system reli-ability assessment. If the multi-level pass/fail data are overlapping, one chal enging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps: fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and final y sim-plifying the likelihood function. An example of a satel ite rol ing control system demonstrates the feasibility and the efficiency of the proposed approach.
On some method of the space elevator maximum stress reduction
Directory of Open Access Journals (Sweden)
Ambartsumian S. A.
2007-03-01
Full Text Available The possibility of the realization and exploitation of the space elevator project is connected with a number of complicated problems. One of them are large elastic stresses arising in the space elevator ribbon body, which are considerably bigger that the limit of strength of modern materials. This note is devoted to the solution of problem of maximum stress reduction in the ribbon by the modification of the ribbon cross-section area.
Laínez, José M; Orcun, Seza; Pekny, Joseph F; Reklaitis, Gintaras V; Suvannasankha, Attaya; Fausel, Christopher; Anaissie, Elias J; Blau, Gary E
2014-01-01
Variable metabolism, dose-dependent efficacy, and a narrow therapeutic target of cyclophosphamide (CY) suggest that dosing based on individual pharmacokinetics (PK) will improve efficacy and minimize toxicity. Real-time individualized CY dose adjustment was previously explored using a maximum a posteriori (MAP) approach based on a five serum-PK sampling in patients with hematologic malignancy undergoing stem cell transplantation. The MAP approach resulted in an improved toxicity profile without sacrificing efficacy. However, extensive PK sampling is costly and not generally applicable in the clinic. We hypothesize that the assumption-free Bayesian approach (AFBA) can reduce sampling requirements, while improving the accuracy of results. Retrospective analysis of previously published CY PK data from 20 patients undergoing stem cell transplantation. In that study, Bayesian estimation based on the MAP approach of individual PK parameters was accomplished to predict individualized day-2 doses of CY. Based on these data, we used the AFBA to select the optimal sampling schedule and compare the projected probability of achieving the therapeutic end points. By optimizing the sampling schedule with the AFBA, an effective individualized PK characterization can be obtained with only two blood draws at 4 and 16 hours after administration on day 1. The second-day doses selected with the AFBA were significantly different than the MAP approach and averaged 37% higher probability of attaining the therapeutic targets. The AFBA, based on cutting-edge statistical and mathematical tools, allows an accurate individualized dosing of CY, with simplified PK sampling. This highly accessible approach holds great promise for improving efficacy, reducing toxicities, and lowering treatment costs. © 2013 Pharmacotherapy Publications, Inc.
The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction
DEFF Research Database (Denmark)
Sabuncu, Mert R.; Van Leemput, Koen
2011-01-01
This paper presents the Relevance VoxelMachine (RVoxM), a Bayesian multivariate pattern analysis (MVPA) algorithm that is specifically designed for making predictions based on image data. In contrast to generic MVPA algorithms that have often been used for this purpose, the method is designed to ...
Bayat, Sahar; Cuggia, Marc; Kessler, Michel; Briançon, Serge; Le Beux, Pierre; Frimat, Luc
2008-01-01
Evaluation of adult candidates for kidney transplantation diverges from one centre to another. Our purpose was to assess the suitability of Bayesian method for describing the factors associated to registration on the waiting list in a French healthcare network. We have found no published paper using Bayesian method in this domain. Eight hundred and nine patients starting renal replacement therapy were included in the analysis. The data were extracted from the information system of the healthcare network. We performed conventional statistical analysis and data mining analysis using mainly Bayesian networks. The Bayesian model showed that the probability of registration on the waiting list is associated to age, cardiovascular disease, diabetes, serum albumin level, respiratory disease, physical impairment, follow-up in the department performing transplantation and past history of malignancy. These results are similar to conventional statistical method. The comparison between conventional analysis and data mining analysis showed us the contribution of the data mining method for sorting variables and having a global view of the variables' associations. Moreover theses approaches constitute an essential step toward a decisional information system for healthcare networks.
A Bayesian MCMC method for point process models with intractable normalising constants
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
2004-01-01
to simulate from the "unknown distribution", perfect simulation algorithms become useful. We illustrate the method in cases whre the likelihood is given by a Markov point process model. Particularly, we consider semi-parametric Bayesian inference in connection to both inhomogeneous Markov point process models...
Landslide hazards mapping using uncertain Naïve Bayesian classification method
Institute of Scientific and Technical Information of China (English)
毛伊敏; 张茂省; 王根龙; 孙萍萍
2015-01-01
Landslide hazard mapping is a fundamental tool for disaster management activities in Loess terrains. Aiming at major issues with these landslide hazard assessment methods based on Naïve Bayesian classification technique, which is difficult in quantifying those uncertain triggering factors, the main purpose of this work is to evaluate the predictive power of landslide spatial models based on uncertain Naïve Bayesian classification method in Baota district of Yan’an city in Shaanxi province, China. Firstly, thematic maps representing various factors that are related to landslide activity were generated. Secondly, by using field data and GIS techniques, a landslide hazard map was performed. To improve the accuracy of the resulting landslide hazard map, the strategies were designed, which quantified the uncertain triggering factor to design landslide spatial models based on uncertain Naïve Bayesian classification method named NBU algorithm. The accuracies of the area under relative operating characteristics curves (AUC) in NBU and Naïve Bayesian algorithm are 87.29%and 82.47%respectively. Thus, NBU algorithm can be used efficiently for landslide hazard analysis and might be widely used for the prediction of various spatial events based on uncertain classification technique.
METHOD FOR DETERMINING THE MAXIMUM ARRANGEMENT FACTOR OF FOOTWEAR PARTS
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DRIŞCU Mariana
2014-05-01
Full Text Available By classic methodology, designing footwear is a very complex and laborious activity. That is because classic methodology requires many graphic executions using manual means, which consume a lot of the producer’s time. Moreover, the results of this classical methodology may contain many inaccuracies with the most unpleasant consequences for the footwear producer. Thus, the costumer that buys a footwear product by taking into consideration the characteristics written on the product (size, width can notice after a period that the product has flaws because of the inadequate design. In order to avoid this kind of situations, the strictest scientific criteria must be followed when one designs a footwear product. The decisive step in this way has been made some time ago, when, as a result of powerful technical development and massive implementation of electronical calculus systems and informatics, This paper presents a product software for determining all possible arrangements of a footwear product’s reference points, in order to automatically acquire the maximum arrangement factor. The user multiplies the pattern in order to find the economic arrangement for the reference points. In this purpose, the user must probe few arrangement variants, in the translation and rotate-translation system. The same process is used in establishing the arrangement factor for the two points of reference of the designed footwear product. After probing several variants of arrangement in the translation and rotation and translation systems, the maximum arrangement factors are chosen. This allows the user to estimate the material wastes.
Research on Evaluation Method Based on Modified Buckley Decision Making and Bayesian Network
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Neng-pu Yang
2015-01-01
Full Text Available This work presents a novel evaluation method, which can be applied in the field of risk assessment, project management, cause analysis, and so forth. Two core technologies are used in the method, namely, modified Buckley Decision Making and Bayesian Network. Based on the modified Buckley Decision Making, the fuzzy probabilities of element factors are calibrated. By the forward and backward calculation of Bayesian Network, the structure importance, probability importance, and criticality importance of each factor are calculated and discussed. A numerical example of risk evaluation for dangerous goods transport process is given to verify the method. The results indicate that the method can efficiently identify the weakest element factor. In addition, the method can improve the reliability and objectivity for evaluation.
A method of spherical harmonic analysis in the geosciences via hierarchical Bayesian inference
Muir, J. B.; Tkalčić, H.
2015-11-01
The problem of decomposing irregular data on the sphere into a set of spherical harmonics is common in many fields of geosciences where it is necessary to build a quantitative understanding of a globally varying field. For example, in global seismology, a compressional or shear wave speed that emerges from tomographic images is used to interpret current state and composition of the mantle, and in geomagnetism, secular variation of magnetic field intensity measured at the surface is studied to better understand the changes in the Earth's core. Optimization methods are widely used for spherical harmonic analysis of irregular data, but they typically do not treat the dependence of the uncertainty estimates on the imposed regularization. This can cause significant difficulties in interpretation, especially when the best-fit model requires more variables as a result of underestimating data noise. Here, with the above limitations in mind, the problem of spherical harmonic expansion of irregular data is treated within the hierarchical Bayesian framework. The hierarchical approach significantly simplifies the problem by removing the need for regularization terms and user-supplied noise estimates. The use of the corrected Akaike Information Criterion for picking the optimal maximum degree of spherical harmonic expansion and the resulting spherical harmonic analyses are first illustrated on a noisy synthetic data set. Subsequently, the method is applied to two global data sets sensitive to the Earth's inner core and lowermost mantle, consisting of PKPab-df and PcP-P differential traveltime residuals relative to a spherically symmetric Earth model. The posterior probability distributions for each spherical harmonic coefficient are calculated via Markov Chain Monte Carlo sampling; the uncertainty obtained for the coefficients thus reflects the noise present in the real data and the imperfections in the spherical harmonic expansion.
Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data.
Tian, Tianhai
2016-01-01
The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data.
A Bayesian method to estimate the neutron response matrix of a single crystal CVD diamond detector
Energy Technology Data Exchange (ETDEWEB)
Reginatto, Marcel; Araque, Jorge Guerrero; Nolte, Ralf; Zbořil, Miroslav; Zimbal, Andreas [Physikalisch-Technische Bundesanstalt, D-38116 Braunschweig (Germany); Gagnon-Moisan, Francis [Paul Scherrer Institut, CH-5232 Villigen (Switzerland)
2015-01-13
Detectors made from artificial chemical vapor deposition (CVD) single crystal diamond are very promising candidates for applications where high resolution neutron spectrometry in very high neutron fluxes is required, for example in fusion research. We propose a Bayesian method to estimate the neutron response function of the detector for a continuous range of neutron energies (in our case, 10 MeV ≤ E{sub n} ≤ 16 MeV) based on a few measurements with quasi-monoenergetic neutrons. This method is needed because a complete set of measurements is not available and the alternative approach of using responses based on Monte Carlo calculations is not feasible. Our approach uses Bayesian signal-background separation techniques and radial basis function interpolation methods. We present the analysis of data measured at the PTB accelerator facility PIAF. The method is quite general and it can be applied to other particle detectors with similar characteristics.
Directory of Open Access Journals (Sweden)
Kodner Robin B
2010-10-01
Full Text Available Abstract Background Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets. Results This paper introduces pplacer, a software package for phylogenetic placement and subsequent visualization. The algorithm can place twenty thousand short reads on a reference tree of one thousand taxa per hour per processor, has essentially linear time and memory complexity in the number of reference taxa, and is easy to run in parallel. Pplacer features calculation of the posterior probability of a placement on an edge, which is a statistically rigorous way of quantifying uncertainty on an edge-by-edge basis. It also can inform the user of the positional uncertainty for query sequences by calculating expected distance between placement locations, which is crucial in the estimation of uncertainty with a well-sampled reference tree. The software provides visualizations using branch thickness and color to represent number of placements and their uncertainty. A simulation study using reads generated from 631 COG alignments shows a high level of accuracy for phylogenetic placement over a wide range of alignment diversity, and the power of edge uncertainty estimates to measure placement confidence. Conclusions Pplacer enables efficient phylogenetic placement and subsequent visualization, making likelihood-based phylogenetics methodology practical for large collections of reads; it is freely available as source code, binaries, and a web service.
Radiation dose reduction in computed tomography perfusion using spatial-temporal Bayesian methods
Fang, Ruogu; Raj, Ashish; Chen, Tsuhan; Sanelli, Pina C.
2012-03-01
In current computed tomography (CT) examinations, the associated X-ray radiation dose is of significant concern to patients and operators, especially CT perfusion (CTP) imaging that has higher radiation dose due to its cine scanning technique. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) parameter as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and degrade CT perfusion maps greatly if no adequate noise control is applied during image reconstruction. To capture the essential dynamics of CT perfusion, a simple spatial-temporal Bayesian method that uses a piecewise parametric model of the residual function is used, and then the model parameters are estimated from a Bayesian formulation of prior smoothness constraints on perfusion parameters. From the fitted residual function, reliable CTP parameter maps are obtained from low dose CT data. The merit of this scheme exists in the combination of analytical piecewise residual function with Bayesian framework using a simpler prior spatial constrain for CT perfusion application. On a dataset of 22 patients, this dynamic spatial-temporal Bayesian model yielded an increase in signal-tonoise-ratio (SNR) of 78% and a decrease in mean-square-error (MSE) of 40% at low dose radiation of 43mA.
A Fully Bayesian Method for Jointly Fitting Instrumental Calibration and X-Ray Spectral Models
Xu, Jin; van Dyk, David A.; Kashyap, Vinay L.; Siemiginowska, Aneta; Connors, Alanna; Drake, Jeremy; Meng, Xiao-Li; Ratzlaff, Pete; Yu, Yaming
2014-10-01
Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is "pragmatic" in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use a principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.
A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models
Energy Technology Data Exchange (ETDEWEB)
Xu, Jin; Yu, Yaming [Department of Statistics, University of California, Irvine, Irvine, CA 92697-1250 (United States); Van Dyk, David A. [Statistics Section, Imperial College London, Huxley Building, South Kensington Campus, London SW7 2AZ (United Kingdom); Kashyap, Vinay L.; Siemiginowska, Aneta; Drake, Jeremy; Ratzlaff, Pete [Smithsonian Astrophysical Observatory, 60 Garden Street, Cambridge, MA 02138 (United States); Connors, Alanna; Meng, Xiao-Li, E-mail: jinx@uci.edu, E-mail: yamingy@ics.uci.edu, E-mail: dvandyk@imperial.ac.uk, E-mail: vkashyap@cfa.harvard.edu, E-mail: asiemiginowska@cfa.harvard.edu, E-mail: jdrake@cfa.harvard.edu, E-mail: pratzlaff@cfa.harvard.edu, E-mail: meng@stat.harvard.edu [Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138 (United States)
2014-10-20
Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use a principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.
Feranec, Robert S.; Kozlowski, Andrew L.
2016-03-01
To understand what factors control species colonization and extirpation within specific paleoecosystems, we analyzed radiocarbon dates of megafaunal mammal species from New York State after the Last Glacial Maximum. We hypothesized that the timing of colonization and extirpation were both driven by access to preferred habitat types. Bayesian calibration of a database of 39 radiocarbon dates shows that caribou (Rangifer tarandus) were the first colonizers, then mammoth (Mammuthus sp.), and finally American mastodon (Mammut americanum). The timing of colonization cannot reject the hypothesis that colonizing megafauna tracked preferred habitats, as caribou and mammoth arrived when tundra was present, while mastodon arrived after boreal forest was prominent in the state. The timing of caribou colonization implies that ecosystems were developed in the state prior to 16,000 cal yr BP. The contemporaneous arrival of American mastodon with Sporormiella spore decline suggests the dung fungus spore is not an adequate indicator of American mastodon population size. The pattern in the timing of extirpation is opposite to that of colonization. The lack of environmental changes suspected to be ecologically detrimental to American mastodon and mammoth coupled with the arrival of humans shortly before extirpation suggests an anthropogenic cause in the loss of the analyzed species.
Directory of Open Access Journals (Sweden)
Hamid Reza Khalkhali
2016-09-01
Full Text Available Background Often, there is no access to sufficient sample size to estimate the prevalence using the method of direct estimator in all areas. The aim of this study was to compare small area’s Bayesian method and direct method in estimating the prevalence of steatosis in obese and overweight children. Materials and Methods: In this cross-sectional study, was conducted on 150 overweight and obese children aged 2 to 15 years referred to the Children's digestive clinic of Urmia University of Medical Sciences- Iran, in 2013. After Body mass index (BMI calculation, children with overweight and obese were assessed in terms of primary tests of obesity screening. Then children with steatosis confirmed by abdominal Ultrasonography, were referred to the laboratory for doing further tests. Steatosis prevalence was estimated by direct and Bayesian method and their efficiency were evaluated using mean-square error Jackknife method. The study data was analyzed using the open BUGS3.1.2 and R2.15.2 software. Results: The findings indicated that estimation of steatosis prevalence in children using Bayesian and direct methods were between 0.3098 to 0.493, and 0.355 to 0.560 respectively, in Health Districts; 0.3098 to 0.502, and 0.355 to 0.550 in Education Districts; 0.321 to 0.582, and 0.357 to 0.615 in age groups; 0.313 to 0.429, and 0.383 to 0.536 in sex groups. In general, according to the results, mean-square error of Bayesian estimation was smaller than direct estimation (P
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
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.
Method to Determine Maximum Allowable Sinterable Silver Interconnect Size
Energy Technology Data Exchange (ETDEWEB)
Wereszczak, A. A.; Modugno, M. C.; Waters, S. B.; DeVoto, D. J.; Paret, P. P.
2016-05-01
The use of sintered-silver for large-area interconnection is attractive for some large-area bonding applications in power electronics such as the bonding of metal-clad, electrically-insulating substrates to heat sinks. Arrays of different pad sizes and pad shapes have been considered for such large area bonding; however, rather than arbitrarily choosing their size, it is desirable to use the largest size possible where the onset of interconnect delamination does not occur. If that is achieved, then sintered-silver's high thermal and electrical conductivities can be fully taken advantage of. Toward achieving this, a simple and inexpensive proof test is described to identify the largest achievable interconnect size with sinterable silver. The method's objective is to purposely initiate failure or delamination. Copper and invar (a ferrous-nickel alloy whose coefficient of thermal expansion (CTE) is similar to that of silicon or silicon carbide) disks were used in this study and sinterable silver was used to bond them. As a consequence of the method's execution, delamination occurred in some samples during cooling from the 250 degrees C sintering temperature to room temperature and bonding temperature and from thermal cycling in others. These occurrences and their interpretations highlight the method's utility, and the herein described results are used to speculate how sintered-silver bonding will work with other material combinations.
A Bayesian hybrid method for context-sensitive spelling correction
Golding, A R
1996-01-01
Two classes of methods have been shown to be useful for resolving lexical ambiguity. The first relies on the presence of particular words within some distance of the ambiguous target word; the second uses the pattern of words and part-of-speech tags around the target word. These methods have complementary coverage: the former captures the lexical ``atmosphere'' (discourse topic, tense, etc.), while the latter captures local syntax. Yarowsky has exploited this complementarity by combining the two methods using decision lists. The idea is to pool the evidence provided by the component methods, and to then solve a target problem by applying the single strongest piece of evidence, whatever type it happens to be. This paper takes Yarowsky's work as a starting point, applying decision lists to the problem of context-sensitive spelling correction. Decision lists are found, by and large, to outperform either component method. However, it is found that further improvements can be obtained by taking into account not ju...
Using hierarchical Bayesian methods to examine the tools of decision-making
Directory of Open Access Journals (Sweden)
Michael D. Lee
2011-12-01
Full Text Available Hierarchical Bayesian methods offer a principled and comprehensive way to relate psychological models to data. Here we use them to model the patterns of information search, stopping and deciding in a simulated binary comparison judgment task. The simulation involves 20 subjects making 100 forced choice comparisons about the relative magnitudes of two objects (which of two German cities has more inhabitants. Two worked-examples show how hierarchical models can be developed to account for and explain the diversity of both search and stopping rules seen across the simulated individuals. We discuss how the results provide insight into current debates in the literature on heuristic decision making and argue that they demonstrate the power and flexibility of hierarchical Bayesian methods in modeling human decision-making.
Bayesian methods for model uncertainty analysis with application to future sea level rise
Energy Technology Data Exchange (ETDEWEB)
Patwardhan, A.; Small, M.J. (Carnegie Mellon Univ., Pittsburgh, PA (United States))
1992-12-01
This paper addresses the use of data for identifying and characterizing uncertainties in model parameters and predictions. The Bayesian Monte Carlo method is formally presented and elaborated, and applied to the analysis of the uncertainty in a predictive model for global mean sea level change. The method uses observations of output variables, made with an assumed error structure, to determine a posterior distribution of model outputs. This is used to derive a posterior distribution for the model parameters. Results demonstrate the resolution of the uncertainty that is obtained as a result of the Bayesian analysis and also indicate the key contributors to the uncertainty in the sea level rise model. While the technique is illustrated with a simple, preliminary model, the analysis provides an iterative framework for model refinement. The methodology developed in this paper provides a mechanism for the incorporation of ongoing data collection and research in decision-making for problems involving uncertain environmental change.
Mendes, B. S.; Draper, D.
2008-12-01
The issue of model uncertainty and model choice is central in any groundwater modeling effort [Neuman and Wierenga, 2003]; among the several approaches to the problem we favour using Bayesian statistics because it is a method that integrates in a natural way uncertainties (arising from any source) and experimental data. In this work, we experiment with several Bayesian approaches to model choice, focusing primarily on demonstrating the usefulness of the Reversible Jump Markov Chain Monte Carlo (RJMCMC) simulation method [Green, 1995]; this is an extension of the now- common MCMC methods. Standard MCMC techniques approximate posterior distributions for quantities of interest, often by creating a random walk in parameter space; RJMCMC allows the random walk to take place between parameter spaces with different dimensionalities. This fact allows us to explore state spaces that are associated with different deterministic models for experimental data. Our work is exploratory in nature; we restrict our study to comparing two simple transport models applied to a data set gathered to estimate the breakthrough curve for a tracer compound in groundwater. One model has a mean surface based on a simple advection dispersion differential equation; the second model's mean surface is also governed by a differential equation but in two dimensions. We focus on artificial data sets (in which truth is known) to see if model identification is done correctly, but we also address the issues of over and under-paramerization, and we compare RJMCMC's performance with other traditional methods for model selection and propagation of model uncertainty, including Bayesian model averaging, BIC and DIC.References Neuman and Wierenga (2003). A Comprehensive Strategy of Hydrogeologic Modeling and Uncertainty Analysis for Nuclear Facilities and Sites. NUREG/CR-6805, Division of Systems Analysis and Regulatory Effectiveness Office of Nuclear Regulatory Research, U. S. Nuclear Regulatory Commission
A note on the robustness of a full Bayesian method for nonignorable missing data analysis
Zhang, Zhiyong; Wang,Lijuan
2012-01-01
A full Bayesian method utilizing data augmentation and Gibbs sampling algorithms is presented for analyzing nonignorable missing data. The discussion focuses on a simplified selection model for regression analysis. Regardless of missing mechanisms, it is assumed that missingness only depends on the missing variable itself. Simulation results demonstrate that the simplified selection model can recover regression model parameters under both correctly specified situations and many misspecified s...
Fission yield covariances for JEFF: A Bayesian Monte Carlo method
Directory of Open Access Journals (Sweden)
Leray Olivier
2017-01-01
Full Text Available The JEFF library does not contain fission yield covariances, but simply best estimates and uncertainties. This situation is not unique as all libraries are facing this deficiency, firstly due to the lack of a defined format. An alternative approach is to provide a set of random fission yields, themselves reflecting covariance information. In this work, these random files are obtained combining the information from the JEFF library (fission yields and uncertainties and the theoretical knowledge from the GEF code. Examples of this method are presented for the main actinides together with their impacts on simple burn-up and decay heat calculations.
Directory of Open Access Journals (Sweden)
Stephen W Hartley
2012-09-01
Full Text Available Genome-wide association studies (GWAS have identified numerous associations between genetic loci and individual phenotypes; however, relatively few GWAS have attempted to detect pleiotropic associations, in which loci are simultaneously associated with multiple distinct phenotypes. We show that pleiotropic associations can be directly modeled via the construction of simple Bayesian networks, and that these models can be applied to produce single or ensembles of Bayesian classifiers that leverage pleiotropy to improve genetic risk prediction.The proposed method includes two phases: (1 Bayesian model comparison, to identify SNPs associated with one or more traits; and (2 cross validation feature selection, in which a final set of SNPs is selected to optimize prediction.To demonstrate the capabilities and limitations of the method, a total of 1600 case-control GWAS datasets with 2 dichotomous phenotypes were simulated under 16 scenarios, varying the association strengths of causal SNPs, the size of the discovery sets, the balance between cases and controls, and the number of pleiotropic causal SNPs.Across the 16 scenarios, prediction accuracy varied from 90% to 50%. In the 14 scenarios that included pleiotropically-associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction. In the 2 scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal.
Introduction to Bayesian statistics
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...
Bayesian Blocks, A New Method to Analyze Structure in Photon Counting Data
Scargle, J D
1997-01-01
I describe a new time-domain algorithm for detecting localized structures (bursts), revealing pulse shapes, and generally characterizing intensity variations. The input is raw counting data, in any of three forms: time-tagged photon events (TTE), binned counts, or time-to-spill (TTS) data. The output is the most likely segmentation of the observation into time intervals during which the photon arrival rate is perceptibly constant -- i.e. has a fixed intensity without statistically significant variations. Since the analysis is based on Bayesian statistics, I call the resulting structures Bayesian Blocks. Unlike most, this method does not stipulate time bins -- instead the data themselves determine a piecewise constant representation. Therefore the analysis procedure itself does not impose a lower limit to the time scale on which variability can be detected. Locations, amplitudes, and rise and decay times of pulses within a time series can be estimated, independent of any pulse-shape model -- but only if they d...
Dorn, C; Khan, A; Heng, K; Alibert, Y; Helled, R; Rivoldini, A; Benz, W
2016-01-01
We aim to present a generalized Bayesian inference method for constraining interiors of super Earths and sub-Neptunes. Our methodology succeeds in quantifying the degeneracy and correlation of structural parameters for high dimensional parameter spaces. Specifically, we identify what constraints can be placed on composition and thickness of core, mantle, ice, ocean, and atmospheric layers given observations of mass, radius, and bulk refractory abundance constraints (Fe, Mg, Si) from observations of the host star's photospheric composition. We employed a full probabilistic Bayesian inference analysis that formally accounts for observational and model uncertainties. Using a Markov chain Monte Carlo technique, we computed joint and marginal posterior probability distributions for all structural parameters of interest. We included state-of-the-art structural models based on self-consistent thermodynamics of core, mantle, high-pressure ice, and liquid water. Furthermore, we tested and compared two different atmosp...
Bauwens, Luc; Korobilis, Dimitris
2011-01-01
This comprehensive Handbook presents the current state of art in the theory and methodology of macroeconomic data analysis. It is intended as a reference for graduate students and researchers interested in exploring new methodologies, but can also be employed as a graduate text. The Handbook concentrates on the most important issues, models and techniques for research in macroeconomics, and highlights the core methodologies and their empirical application in an accessible manner. Each chapter...
A method for Bayesian estimation of the probability of local intensity for some cities in Japan
Directory of Open Access Journals (Sweden)
G. C. Koravos
2002-06-01
Full Text Available Seismic hazard in terms of probability of exceedance of a given intensity in a given time span,was assessed for 12 sites in Japan.The method does not use any attenuation law.Instead,the dependence of local intensity on epicentral intensity I 0 is calculated directly from the data,using a Bayesian model.According to this model (Meroni et al., 1994,local intensity follows the binomial distribution with parameters (I 0 ,p .The parameter p is considered as a random variable following the Beta distribution.This manner of Bayesian estimates of p are assessed for various values of epicentral intensity and epicentral distance.In order to apply this model for the assessment of seismic hazard,the area under consideration is divided into seismic sources (zonesof known seismicity.The contribution of each source on the seismic hazard at every site is calculated according to the Bayesian model and the result is the combined effect of all the sources.High probabilities of exceedance were calculated for the sites that are in the central part of the country,with hazard decreasing slightly towards the north and the south parts.
Alkhamis, Mohammad A; Perez, Andres M; Murtaugh, Michael P; Wang, Xiong; Morrison, Robert B
2016-01-01
Classical phylogenetic methods such as neighbor-joining or maximum likelihood trees, provide limited inferences about the evolution of important pathogens and ignore important evolutionary parameters and uncertainties, which in turn limits decision making related to surveillance, control, and prevention resources. Bayesian phylodynamic models have recently been used to test research hypotheses related to evolution of infectious agents. However, few studies have attempted to model the evolutionary dynamics of porcine reproductive and respiratory syndrome virus (PRRSV) and, to the authors' knowledge, no attempt has been made to use large volumes of routinely collected data, sometimes referred to as big data, in the context of animal disease surveillance. The objective of this study was to explore and discuss the applications of Bayesian phylodynamic methods for modeling the evolution and spread of a notable 1-7-4 RFLP-type PRRSV between 2014 and 2015. A convenience sample of 288 ORF5 sequences was collected from 5 swine production systems in the United States between September 2003 and March 2015. Using coalescence and discrete trait phylodynamic models, we were able to infer population growth and demographic history of the virus, identified the most likely ancestral system (root state posterior probability = 0.95) and revealed significant dispersal routes (Bayes factor > 6) of viral exchange among systems. Results indicate that currently circulating viruses are evolving rapidly, and show a higher level of relative genetic diversity over time, when compared to earlier relatives. Biological soundness of model results is supported by the finding that sow farms were responsible for PRRSV spread within the systems. Such results cannot be obtained by traditional phylogenetic methods, and therefore, our results provide a methodological framework for molecular epidemiological modeling of new PRRSV outbreaks and demonstrate the prospects of phylodynamic models to inform
Directory of Open Access Journals (Sweden)
Mohammad A. Alkhamis
2016-02-01
Full Text Available Classical phylogenetic methods such as neighbor-joining or maximum likelihood trees, provide limited inferences about the evolution of important pathogens and ignore important evolutionary parameters and uncertainties, which in turn limits decision making related to surveillance, control and prevention resources. Bayesian phylodynamic models have recently been used to test research hypothesis related to evolution of infectious agents. However, few studies have attempted to model the evolutionary dynamics of porcine reproductive and respiratory syndrome virus (PRRSV and, to the authors’ knowledge, no attempt has been made to use large volumes of routinely collected data, sometimes referred to as big data, in the context of animal disease surveillance. The objective of this study was to explore and discuss the applications of Bayesian phylodynamic methods for modeling the evolution and spread of a notable 1-7-4 RFLP-type PRRSV between 2014 and 2015. A convenience sample of 288 ORF5 sequences was collected from 5 swine production systems in the United States between September 2003 and March 2015. Using coalescence and discrete trait phylodynamic models, we were able to infer population growth and demographic history of the virus, identified the most likely ancestral system (root state posterior probability = 0.95 and revealed significant dispersal routes (Bayes factor > 6 of viral exchange among systems. Results indicate that currently circulating viruses are evolving rapidly, and show a higher level of relative genetic diversity over time, when compared to earlier relatives. Biological soundness of model results is supported by the finding that sow farms were responsible for PRRSV spread within the systems. Such results can’t be obtained by traditional phylogenetic methods, and therefore, our results provide a methodological framework for molecular epidemiological modeling of new PRRSV outbreaks and demonstrate the prospects of phylodynamic
Dimitrakakis, Christos
2009-01-01
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most problems of interest, the optimal solution involves planning in an infinitely large tree. However, it is possible to obtain stochastic lower and upper bounds on the value of each tree node. This enables us to use stochastic branch and bound algorithms to search the tree efficiently. This paper proposes two such algorithms and examines their complexity in this setting.
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
Evaluation of the antibacterial residue surveillance programme in Danish pigs using Bayesian methods
DEFF Research Database (Denmark)
Freitas de Matos Baptista, Filipa; Alban, L.; Olsen, A. M.;
2012-01-01
Residues of pharmacological active substances or their metabolites might be found in food products from food-producing animals. Maximum Residue Limits for pharmacological active substances in foodstuffs of animal origin are established to assure high food safety standards. Each year, more than 20......,000 samples are analysed for the presence of antibacterial residues in Danish pigs. This corresponds to 0.1% of the size of the slaughter pig population and more than 1% of the sows slaughtered. In this study, a Bayesian model was used to evaluate the Danish surveillance system accuracy and to investigate...... increasing or maintaining the probability of detection. Hence, the antibacterial residue surveillance programme in Danish pigs would be more cost-effective than today....
A Bayesian design space for analytical methods based on multivariate models and predictions.
Lebrun, Pierre; Boulanger, Bruno; Debrus, Benjamin; Lambert, Philippe; Hubert, Philippe
2013-01-01
The International Conference for Harmonization (ICH) has released regulatory guidelines for pharmaceutical development. In the document ICH Q8, the design space of a process is presented as the set of factor settings providing satisfactory results. However, ICH Q8 does not propose any practical methodology to define, derive, and compute design space. In parallel, in the last decades, it has been observed that the diversity and the quality of analytical methods have evolved exponentially, allowing substantial gains in selectivity and sensitivity. However, there is still a lack of a rationale toward the development of robust separation methods in a systematic way. Applying ICH Q8 to analytical methods provides a methodology for predicting a region of the space of factors in which results will be reliable. Combining design of experiments and Bayesian standard multivariate regression, an identified form of the predictive distribution of a new response vector has been identified and used, under noninformative as well as informative prior distributions of the parameters. From the responses and their predictive distribution, various critical quality attributes can be easily derived. This Bayesian framework was then extended to the multicriteria setting to estimate the predictive probability that several critical quality attributes will be jointly achieved in the future use of an analytical method. An example based on a high-performance liquid chromatography (HPLC) method is given. For this example, a constrained sampling scheme was applied to ensure the modeled responses have desirable properties.
Bayesian methods for uncertainty factor application for derivation of reference values.
Simon, Ted W; Zhu, Yiliang; Dourson, Michael L; Beck, Nancy B
2016-10-01
In 2014, the National Research Council (NRC) published Review of EPA's Integrated Risk Information System (IRIS) Process that considers methods EPA uses for developing toxicity criteria for non-carcinogens. These criteria are the Reference Dose (RfD) for oral exposure and Reference Concentration (RfC) for inhalation exposure. The NRC Review suggested using Bayesian methods for application of uncertainty factors (UFs) to adjust the point of departure dose or concentration to a level considered to be without adverse effects for the human population. The NRC foresaw Bayesian methods would be potentially useful for combining toxicity data from disparate sources-high throughput assays, animal testing, and observational epidemiology. UFs represent five distinct areas for which both adjustment and consideration of uncertainty may be needed. NRC suggested UFs could be represented as Bayesian prior distributions, illustrated the use of a log-normal distribution to represent the composite UF, and combined this distribution with a log-normal distribution representing uncertainty in the point of departure (POD) to reflect the overall uncertainty. Here, we explore these suggestions and present a refinement of the methodology suggested by NRC that considers each individual UF as a distribution. From an examination of 24 evaluations from EPA's IRIS program, when individual UFs were represented using this approach, the geometric mean fold change in the value of the RfD or RfC increased from 3 to over 30, depending on the number of individual UFs used and the sophistication of the assessment. We present example calculations and recommendations for implementing the refined NRC methodology.
Bayesian methods for the conformational classification of eight-membered rings
DEFF Research Database (Denmark)
Pérez, J.; Nolsøe, Kim; Kessler, M.
2005-01-01
Two methods for the classification of eight-membered rings based on a Bayesian analysis are presented. The two methods share the same probabilistic model for the measurement of torsion angles, but while the first method uses the canonical forms of cyclooctane and, given an empirical sequence...... of eight torsion angles, yields the probability that the associated structure corresponds to each of the ten canonical conformations, the second method does not assume previous knowledge of existing conformations and yields a clustering classification of a data set, allowing new conformations...... to be detected. Both methods have been tested using the conformational classification of Csp3 eight-membered rings described in the literature. The methods have also been employed to classify the solidstate conformation in Csp3 eight-membered rings using data retrieved from an updated version of the Cambridge...
The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction
DEFF Research Database (Denmark)
Sabuncu, Mert R.; Van Leemput, Koen
2011-01-01
to utilize a small number of spatially clustered sets of voxels that are particularly suited for clinical interpretation. RVoxM automatically tunes all its free parameters during the training phase, and offers the additional advantage of producing probabilistic prediction outcomes. Experiments on age......This paper presents the Relevance VoxelMachine (RVoxM), a Bayesian multivariate pattern analysis (MVPA) algorithm that is specifically designed for making predictions based on image data. In contrast to generic MVPA algorithms that have often been used for this purpose, the method is designed...... prediction from structural brain MRI indicate that RVoxM yields biologically meaningful models that provide excellent predictive accuracy....
Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method.
Zhang, Guanglei; Liu, Fei; Liu, Jie; Luo, Jianwen; Xie, Yaoqin; Bai, Jing; Xing, Lei
2017-01-01
X-ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. Combining the principles of X-ray excitation of luminescence-based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow-beam XLCT, a cone beam XLCT (CB-XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using cone beam X-ray for XLCT is that the inverse problem here is seriously ill-conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB-XLCT reconstruction. The method utilizes a local regularization strategy based on Gaussian Markov random field to mitigate the ill-conditioness of CB-XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm is adopted to reconstruct the image with a voxel-based closed-form solution. Results of numerical simulations and mouse experiments show that the self-adaptive Bayesian method significantly improves the CB-XLCT image quality as compared with conventional methods.
Maximum entropy method for solving operator equations of the first kind
Institute of Scientific and Technical Information of China (English)
金其年; 侯宗义
1997-01-01
The maximum entropy method for linear ill-posed problems with modeling error and noisy data is considered and the stability and convergence results are obtained. When the maximum entropy solution satisfies the "source condition", suitable rates of convergence can be derived. Considering the practical applications, an a posteriori choice for the regularization parameter is presented. As a byproduct, a characterization of the maximum entropy regularized solution is given.
A hybrid solar panel maximum power point search method that uses light and temperature sensors
Ostrowski, Mariusz
2016-04-01
Solar cells have low efficiency and non-linear characteristics. To increase the output power solar cells are connected in more complex structures. Solar panels consist of series of connected solar cells with a few bypass diodes, to avoid negative effects of partial shading conditions. Solar panels are connected to special device named the maximum power point tracker. This device adapt output power from solar panels to load requirements and have also build in a special algorithm to track the maximum power point of solar panels. Bypass diodes may cause appearance of local maxima on power-voltage curve when the panel surface is illuminated irregularly. In this case traditional maximum power point tracking algorithms can find only a local maximum power point. In this article the hybrid maximum power point search algorithm is presented. The main goal of the proposed method is a combination of two algorithms: a method that use temperature sensors to track maximum power point in partial shading conditions and a method that use illumination sensor to track maximum power point in equal illumination conditions. In comparison to another methods, the proposed algorithm uses correlation functions to determinate the relationship between values of illumination and temperature sensors and the corresponding values of current and voltage in maximum power point. In partial shading condition the algorithm calculates local maximum power points bases on the value of temperature and the correlation function and after that measures the value of power on each of calculated point choose those with have biggest value, and on its base run the perturb and observe search algorithm. In case of equal illumination algorithm calculate the maximum power point bases on the illumination value and the correlation function and on its base run the perturb and observe algorithm. In addition, the proposed method uses a special coefficient modification of correlation functions algorithm. This sub
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-08
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.
An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power
Directory of Open Access Journals (Sweden)
Antonio Bracale
2015-09-01
Full Text Available Currently, among renewable distributed generation systems, wind generators are receiving a great deal of interest due to the great economic, technological, and environmental incentives they involve. However, the uncertainties due to the intermittent nature of wind energy make it difficult to operate electrical power systems optimally and make decisions that satisfy the needs of all the stakeholders of the electricity energy market. Thus, there is increasing interest determining how to forecast wind power production accurately. Most the methods that have been published in the relevant literature provided deterministic forecasts even though great interest has been focused recently on probabilistic forecast methods. In this paper, an advanced probabilistic method is proposed for short-term forecasting of wind power production. A mixture of two Weibull distributions was used as a probability function to model the uncertainties associated with wind speed. Then, a Bayesian inference approach with a particularly-effective, autoregressive, integrated, moving-average model was used to determine the parameters of the mixture Weibull distribution. Numerical applications also are presented to provide evidence of the forecasting performance of the Bayesian-based approach.
Connolly, Brian; Cohen, K Bretonnel; Santel, Daniel; Bayram, Ulya; Pestian, John
2017-08-07
Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a likelihood scale. Current state-of-the-art calibration methods are generally accurate and applicable to many ML models, but improved granularity and accuracy of such methods would increase the information available for clinical decision making. This novel non-parametric Bayesian approach is demonstrated on a variety of data sets, including simulated classifier outputs, biomedical data sets from the University of California, Irvine (UCI) Machine Learning Repository, and a clinical data set built to determine suicide risk from the language of emergency department patients. The method is first demonstrated on support-vector machine (SVM) models, which generally produce well-behaved, well understood scores. The method produces calibrations that are comparable to the state-of-the-art Bayesian Binning in Quantiles (BBQ) method when the SVM models are able to effectively separate cases and controls. However, as the SVM models' ability to discriminate classes decreases, our approach yields more granular and dynamic calibrated probabilities comparing to the BBQ method. Improvements in granularity and range are even more dramatic when the discrimination between the classes is artificially degraded by replacing the SVM model with an ad hoc k-means classifier. The method allows both clinicians and patients to have a more nuanced view of the output of an ML model, allowing better decision making. The method is demonstrated on simulated data, various biomedical data sets and a clinical data set, to which diverse ML methods are applied. Trivially extending the method to (non-ML) clinical scores is also discussed.
Maximum entropy method applied to deblurring images on a MasPar MP-1 computer
Bonavito, N. L.; Dorband, John; Busse, Tim
1991-01-01
A statistical inference method based on the principle of maximum entropy is developed for the purpose of enhancing and restoring satellite images. The proposed maximum entropy image restoration method is shown to overcome the difficulties associated with image restoration and provide the smoothest and most appropriate solution consistent with the measured data. An implementation of the method on the MP-1 computer is described, and results of tests on simulated data are presented.
Directory of Open Access Journals (Sweden)
Mroczka Janusz
2014-12-01
Full Text Available Photovoltaic panels have a non-linear current-voltage characteristics to produce the maximum power at only one point called the maximum power point. In the case of the uniform illumination a single solar panel shows only one maximum power, which is also the global maximum power point. In the case an irregularly illuminated photovoltaic panel many local maxima on the power-voltage curve can be observed and only one of them is the global maximum. The proposed algorithm detects whether a solar panel is in the uniform insolation conditions. Then an appropriate strategy of tracking the maximum power point is taken using a decision algorithm. The proposed method is simulated in the environment created by the authors, which allows to stimulate photovoltaic panels in real conditions of lighting, temperature and shading.
Family-based Bayesian collapsing method for rare-variant association study.
He, Liang; Pitkäniemi, Janne M
2014-01-01
In this study, we analyze the Genetic Analysis Workshop 18 data to identify the genes and underlying single-nucleotide polymorphisms on 11 chromosomes that exhibit significant association with systolic blood pressure. We propose a novel family-based method for rare-variant association detection based on the hierarchical Bayesian framework. The method controls spurious associations caused by population stratification, and improves the statistical power to detect not only individual rare variants, but also genes with either continuous or binary outcomes. Our method utilizes nuclear family information, and takes into account the effects of all single-nucleotide polymorphisms in a gene, using a hierarchical model. When we apply this method to the genome-wide Genetic Analysis Workshop 18 data, several genes and single-nucleotide polymorphisms are identified as potentially related to systolic blood pressure.
Directory of Open Access Journals (Sweden)
Frolova A. O.
2012-06-01
Full Text Available Reverse engineering of gene regulatory networks is an intensively studied topic in Systems Biology as it reconstructs regulatory interactions between all genes in the genome in the most complete form. The extreme computational complexity of this problem and lack of thorough reviews on reconstruction methods of gene regulatory network is a significant obstacle to further development of this area. In this article the two most common methods for modeling gene regulatory networks are surveyed: Boolean and Bayesian networks. The mathematical description of each method is given, as well as several algorithmic approaches to modeling gene networks using these methods; the complexity of algorithms and the problems that arise during its implementation are also noted.
Predicting uncertainty in future marine ice sheet volume using Bayesian statistical methods
Davis, A. D.
2015-12-01
The marine ice instability can trigger rapid retreat of marine ice streams. Recent observations suggest that marine ice systems in West Antarctica have begun retreating. However, unknown ice dynamics, computationally intensive mathematical models, and uncertain parameters in these models make predicting retreat rate and ice volume difficult. In this work, we fuse current observational data with ice stream/shelf models to develop probabilistic predictions of future grounded ice sheet volume. Given observational data (e.g., thickness, surface elevation, and velocity) and a forward model that relates uncertain parameters (e.g., basal friction and basal topography) to these observations, we use a Bayesian framework to define a posterior distribution over the parameters. A stochastic predictive model then propagates uncertainties in these parameters to uncertainty in a particular quantity of interest (QoI)---here, the volume of grounded ice at a specified future time. While the Bayesian approach can in principle characterize the posterior predictive distribution of the QoI, the computational cost of both the forward and predictive models makes this effort prohibitively expensive. To tackle this challenge, we introduce a new Markov chain Monte Carlo method that constructs convergent approximations of the QoI target density in an online fashion, yielding accurate characterizations of future ice sheet volume at significantly reduced computational cost.Our second goal is to attribute uncertainty in these Bayesian predictions to uncertainties in particular parameters. Doing so can help target data collection, for the purpose of constraining the parameters that contribute most strongly to uncertainty in the future volume of grounded ice. For instance, smaller uncertainties in parameters to which the QoI is highly sensitive may account for more variability in the prediction than larger uncertainties in parameters to which the QoI is less sensitive. We use global sensitivity
Paul, Sudeshna; Friedman, Alan M; Bailey-Kellogg, Chris; Craig, Bruce A
2013-04-01
The interatomic distance distribution, P(r), is a valuable tool for evaluating the structure of a molecule in solution and represents the maximum structural information that can be derived from solution scattering data without further assumptions. Most current instrumentation for scattering experiments (typically CCD detectors) generates a finely pixelated two-dimensional image. In contin-uation of the standard practice with earlier one-dimensional detectors, these images are typically reduced to a one-dimensional profile of scattering inten-sities, I(q), by circular averaging of the two-dimensional image. Indirect Fourier transformation methods are then used to reconstruct P(r) from I(q). Substantial advantages in data analysis, however, could be achieved by directly estimating the P(r) curve from the two-dimensional images. This article describes a Bayesian framework, using a Markov chain Monte Carlo method, for estimating the parameters of the indirect transform, and thus P(r), directly from the two-dimensional images. Using simulated detector images, it is demonstrated that this method yields P(r) curves nearly identical to the reference P(r). Furthermore, an approach for evaluating spatially correlated errors (such as those that arise from a detector point spread function) is evaluated. Accounting for these errors further improves the precision of the P(r) estimation. Experimental scattering data, where no ground truth reference P(r) is available, are used to demonstrate that this method yields a scattering and detector model that more closely reflects the two-dimensional data, as judged by smaller residuals in cross-validation, than P(r) obtained by indirect transformation of a one-dimensional profile. Finally, the method allows concurrent estimation of the beam center and Dmax, the longest interatomic distance in P(r), as part of the Bayesian Markov chain Monte Carlo method, reducing experimental effort and providing a well defined protocol for these
Photoacoustic discrimination of vascular and pigmented lesions using classical and Bayesian methods
Swearingen, Jennifer A.; Holan, Scott H.; Feldman, Mary M.; Viator, John A.
2010-01-01
Discrimination of pigmented and vascular lesions in skin can be difficult due to factors such as size, subungual location, and the nature of lesions containing both melanin and vascularity. Misdiagnosis may lead to precancerous or cancerous lesions not receiving proper medical care. To aid in the rapid and accurate diagnosis of such pathologies, we develop a photoacoustic system to determine the nature of skin lesions in vivo. By irradiating skin with two laser wavelengths, 422 and 530 nm, we induce photoacoustic responses, and the relative response at these two wavelengths indicates whether the lesion is pigmented or vascular. This response is due to the distinct absorption spectrum of melanin and hemoglobin. In particular, pigmented lesions have ratios of photoacoustic amplitudes of approximately 1.4 to 1 at the two wavelengths, while vascular lesions have ratios of about 4.0 to 1. Furthermore, we consider two statistical methods for conducting classification of lesions: standard multivariate analysis classification techniques and a Bayesian-model-based approach. We study 15 human subjects with eight vascular and seven pigmented lesions. Using the classical method, we achieve a perfect classification rate, while the Bayesian approach has an error rate of 20%.
Buddhachat, Kittisak; Brown, Janine L; Thitaram, Chatchote; Klinhom, Sarisa; Nganvongpanit, Korakot
2017-03-01
As laws tighten to limit commercial ivory trading and protect threatened species like whales and elephants, increased sales of fake ivory products have become widespread. This study describes a method, handheld X-ray fluorescence (XRF) as a noninvasive technique for elemental analysis, to differentiate quickly between ivory (Asian and African elephant, mammoth) from non-ivory (bones, teeth, antler, horn, wood, synthetic resin, rock) materials. An equation consisting of 20 elements and light elements from a stepwise discriminant analysis was used to classify samples, followed by Bayesian binary regression to determine the probability of a sample being 'ivory', with complementary log log analysis to identify the best fit model for this purpose. This Bayesian hybrid classification model was 93% accurate with 92% precision in discriminating ivory from non-ivory materials. The method was then validated by scanning an additional ivory and non-ivory samples, correctly identifying bone as not ivory with >95% accuracy, except elephant bone, which was 72%. It was less accurate for wood and rock (25-85%); however, a preliminary screening to determine if samples are not Ca-dominant could eliminate inorganic materials. In conclusion, elemental analyses by XRF can be used to identify several forms of fake ivory samples, which could have forensic application.
OVarCall: Bayesian Mutation Calling Method Utilizing Overlapping Paired-End Reads.
Moriyama, Takuya; Shiraishi, Yuichi; Chiba, Kenichi; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru
2017-03-01
Detection of somatic mutations from tumor and matched normal sequencing data has become a standard approach in cancer research. Although a number of mutation callers have been developed, it is still difficult to detect mutations with low allele frequency even in exome sequencing. We expect that overlapping paired-end read information is effective for this purpose, but no mutation caller has modeled overlapping information statistically in a proper form in exome sequence data. Here, we develop a Bayesian hierarchical method, OVar- Call (https://github.com/takumorizo/OVarCall), where overlapping paired-end read information improves the accuracy of low allele frequency mutation detection. Firstly, we construct two generative models: one is for reads with somatic variants generated from tumor cells and the other is for reads that does not have somatic variants but potentially includes sequence errors. Secondly, we calculate marginal likelihood for each model using a variational Bayesian algorithm to compute Bayes factor for the detection of somatic mutations. We empirically evaluated the performance of OVarCall and confirmed its better performance than other existing methods.
Estimation model of life insurance claims risk for cancer patients by using Bayesian method
Sukono; Suyudi, M.; Islamiyati, F.; Supian, S.
2017-01-01
This paper discussed the estimation model of the risk of life insurance claims for cancer patients using Bayesian method. To estimate the risk of the claim, the insurance participant data is grouped into two: the number of policies issued and the number of claims incurred. Model estimation is done using a Bayesian approach method. Further, the estimator model was used to estimate the risk value of life insurance claims each age group for each sex. The estimation results indicate that a large risk premium for insured males aged less than 30 years is 0.85; for ages 30 to 40 years is 3:58; for ages 41 to 50 years is 1.71; for ages 51 to 60 years is 2.96; and for those aged over 60 years is 7.82. Meanwhile, for insured women aged less than 30 years was 0:56; for ages 30 to 40 years is 3:21; for ages 41 to 50 years is 0.65; for ages 51 to 60 years is 3:12; and for those aged over 60 years is 9.99. This study is useful in determining the risk premium in homogeneous groups based on gender and age.
Mahmud, Jumailiyah; Sutikno, Muzayanah; Naga, Dali S.
2016-01-01
The aim of this study is to determine variance difference between maximum likelihood and expected A posteriori estimation methods viewed from number of test items of aptitude test. The variance presents an accuracy generated by both maximum likelihood and Bayes estimation methods. The test consists of three subtests, each with 40 multiple-choice…
Maximum energy output of a DFIG wind turbine using an improved MPPT-curve method
Dinh-Chung Phan; Shigeru Yamamoto
2015-01-01
A new method is proposed for obtaining the maximum power output of a doubly-fed induction generator (DFIG) wind turbine to control the rotor- and grid-side converters. The efficiency of maximum power point tracking that is obtained by the proposed method is theoretically guaranteed under assumptions that represent physical conditions. Several control parameters may be adjusted to ensure the quality of control performance. In particular, a DFIG state-space model and a control technique based o...
Directory of Open Access Journals (Sweden)
Velimir Gayevskiy
Full Text Available Bayesian inference methods are extensively used to detect the presence of population structure given genetic data. The primary output of software implementing these methods are ancestry profiles of sampled individuals. While these profiles robustly partition the data into subgroups, currently there is no objective method to determine whether the fixed factor of interest (e.g. geographic origin correlates with inferred subgroups or not, and if so, which populations are driving this correlation. We present ObStruct, a novel tool to objectively analyse the nature of structure revealed in Bayesian ancestry profiles using established statistical methods. ObStruct evaluates the extent of structural similarity between sampled and inferred populations, tests the significance of population differentiation, provides information on the contribution of sampled and inferred populations to the observed structure and crucially determines whether the predetermined factor of interest correlates with inferred population structure. Analyses of simulated and experimental data highlight ObStruct's ability to objectively assess the nature of structure in populations. We show the method is capable of capturing an increase in the level of structure with increasing time since divergence between simulated populations. Further, we applied the method to a highly structured dataset of 1,484 humans from seven continents and a less structured dataset of 179 Saccharomyces cerevisiae from three regions in New Zealand. Our results show that ObStruct provides an objective metric to classify the degree, drivers and significance of inferred structure, as well as providing novel insights into the relationships between sampled populations, and adds a final step to the pipeline for population structure analyses.
Blind Detection of Ultra-faint Streaks with a Maximum Likelihood Method
Dawson, William A; Kamath, Chandrika
2016-01-01
We have developed a maximum likelihood source detection method capable of detecting ultra-faint streaks with surface brightnesses approximately an order of magnitude fainter than the pixel level noise. Our maximum likelihood detection method is a model based approach that requires no a priori knowledge about the streak location, orientation, length, or surface brightness. This method enables discovery of typically undiscovered objects, and enables the utilization of low-cost sensors (i.e., higher-noise data). The method also easily facilitates multi-epoch co-addition. We will present the results from the application of this method to simulations, as well as real low earth orbit observations.
Directory of Open Access Journals (Sweden)
Sarah A Gagliano
Full Text Available The increasing quantity and quality of functional genomic information motivate the assessment and integration of these data with association data, including data originating from genome-wide association studies (GWAS. We used previously described GWAS signals ("hits" to train a regularized logistic model in order to predict SNP causality on the basis of a large multivariate functional dataset. We show how this model can be used to derive Bayes factors for integrating functional and association data into a combined Bayesian analysis. Functional characteristics were obtained from the Encyclopedia of DNA Elements (ENCODE, from published expression quantitative trait loci (eQTL, and from other sources of genome-wide characteristics. We trained the model using all GWAS signals combined, and also using phenotype specific signals for autoimmune, brain-related, cancer, and cardiovascular disorders. The non-phenotype specific and the autoimmune GWAS signals gave the most reliable results. We found SNPs with higher probabilities of causality from functional characteristics showed an enrichment of more significant p-values compared to all GWAS SNPs in three large GWAS studies of complex traits. We investigated the ability of our Bayesian method to improve the identification of true causal signals in a psoriasis GWAS dataset and found that combining functional data with association data improves the ability to prioritise novel hits. We used the predictions from the penalized logistic regression model to calculate Bayes factors relating to functional characteristics and supply these online alongside resources to integrate these data with association data.
Directory of Open Access Journals (Sweden)
Nazia Afreen
2016-03-01
Full Text Available Dengue fever is the most important arboviral disease in the tropical and sub-tropical countries of the world. Delhi, the metropolitan capital state of India, has reported many dengue outbreaks, with the last outbreak occurring in 2013. We have recently reported predominance of dengue virus serotype 2 during 2011-2014 in Delhi. In the present study, we report molecular characterization and evolutionary analysis of dengue serotype 2 viruses which were detected in 2011-2014 in Delhi. Envelope genes of 42 DENV-2 strains were sequenced in the study. All DENV-2 strains grouped within the Cosmopolitan genotype and further clustered into three lineages; Lineage I, II and III. Lineage III replaced lineage I during dengue fever outbreak of 2013. Further, a novel mutation Thr404Ile was detected in the stem region of the envelope protein of a single DENV-2 strain in 2014. Nucleotide substitution rate and time to the most recent common ancestor were determined by molecular clock analysis using Bayesian methods. A change in effective population size of Indian DENV-2 viruses was investigated through Bayesian skyline plot. The study will be a vital road map for investigation of epidemiology and evolutionary pattern of dengue viruses in India.
Hierarchical Bayesian methods for estimation of parameters in a longitudinal HIV dynamic system.
Huang, Yangxin; Liu, Dacheng; Wu, Hulin
2006-06-01
HIV dynamics studies have significantly contributed to the understanding of HIV infection and antiviral treatment strategies. But most studies are limited to short-term viral dynamics due to the difficulty of establishing a relationship of antiviral response with multiple treatment factors such as drug exposure and drug susceptibility during long-term treatment. In this article, a mechanism-based dynamic model is proposed for characterizing long-term viral dynamics with antiretroviral therapy, described by a set of nonlinear differential equations without closed-form solutions. In this model we directly incorporate drug concentration, adherence, and drug susceptibility into a function of treatment efficacy, defined as an inhibition rate of virus replication. We investigate a Bayesian approach under the framework of hierarchical Bayesian (mixed-effects) models for estimating unknown dynamic parameters. In particular, interest focuses on estimating individual dynamic parameters. The proposed methods not only help to alleviate the difficulty in parameter identifiability, but also flexibly deal with sparse and unbalanced longitudinal data from individual subjects. For illustration purposes, we present one simulation example to implement the proposed approach and apply the methodology to a data set from an AIDS clinical trial. The basic concept of the longitudinal HIV dynamic systems and the proposed methodologies are generally applicable to any other biomedical dynamic systems.
Afreen, Nazia; Naqvi, Irshad H; Broor, Shobha; Ahmed, Anwar; Kazim, Syed Naqui; Dohare, Ravins; Kumar, Manoj; Parveen, Shama
2016-03-01
Dengue fever is the most important arboviral disease in the tropical and sub-tropical countries of the world. Delhi, the metropolitan capital state of India, has reported many dengue outbreaks, with the last outbreak occurring in 2013. We have recently reported predominance of dengue virus serotype 2 during 2011-2014 in Delhi. In the present study, we report molecular characterization and evolutionary analysis of dengue serotype 2 viruses which were detected in 2011-2014 in Delhi. Envelope genes of 42 DENV-2 strains were sequenced in the study. All DENV-2 strains grouped within the Cosmopolitan genotype and further clustered into three lineages; Lineage I, II and III. Lineage III replaced lineage I during dengue fever outbreak of 2013. Further, a novel mutation Thr404Ile was detected in the stem region of the envelope protein of a single DENV-2 strain in 2014. Nucleotide substitution rate and time to the most recent common ancestor were determined by molecular clock analysis using Bayesian methods. A change in effective population size of Indian DENV-2 viruses was investigated through Bayesian skyline plot. The study will be a vital road map for investigation of epidemiology and evolutionary pattern of dengue viruses in India.
bcrm: Bayesian Continual Reassessment Method Designs for Phase I Dose-Finding Trials
Directory of Open Access Journals (Sweden)
Michael Sweeting
2013-09-01
Full Text Available This paper presents the R package bcrm for conducting and assessing Bayesian continual reassessment method (CRM designs in Phase I dose-escalation trials. CRM designsare a class of adaptive design that select the dose to be given to the next recruited patient based on accumulating toxicity data from patients already recruited into the trial, often using Bayesian methodology. Despite the original CRM design being proposed in 1990, the methodology is still not widely implemented within oncology Phase I trials. The aim of this paper is to demonstrate, through example of the bcrm package, how a variety of possible designs can be easily implemented within the R statistical software, and how properties of the designs can be communicated to trial investigators using simple textual and graphical output obtained from the package. This in turn should facilitate an iterative process to allow a design to be chosen that is suitable to the needs of the investigator. Our bcrm package is the first to offer a large comprehensive choice of CRM designs, priors and escalation procedures, which can be easily compared and contrasted within the package through the assessment of operating characteristics.
Stochastic margin-based structure learning of Bayesian network classifiers.
Pernkopf, Franz; Wohlmayr, Michael
2013-02-01
The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.
A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules.
Directory of Open Access Journals (Sweden)
Wei Zhang
2010-01-01
Full Text Available Studies of the relationship between DNA variation and gene expression variation, often referred to as "expression quantitative trait loci (eQTL mapping", have been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to facilitate the task, in which co-expressed genes mapped to a common set of markers are treated as a module characterized by latent indicator variables. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulations that this method is more powerful for detecting true eQTLs and their target genes than traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae. Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules corresponding to possibly different biological functions or primary and secondary responses to regulatory perturbations. In addition, we identified nine modules associated with pairs of eQTLs, of which two have been previously reported. We demonstrated that one of the novel modules containing many daughter-cell expressed genes is regulated by AMN1 and BPH1. In conclusion, the Bayesian partition method which simultaneously considers all traits and all markers is more powerful for detecting both pleiotropic and epistatic effects based on both simulated and empirical data.
Directory of Open Access Journals (Sweden)
A.Tuti Rumiati
2012-02-01
Full Text Available This paper discusses Bayesian Method of Small Area Estimation (SAE based on Binomial response variable. SAE method being developed to estimate parameter in small area due to insufficiency of sample. The case study is literacy rate estimation at sub-district level in Sumenep district, East Java Province. Literacy rate is measured by proportion of people who are able to read and write, from the population of 10 year-old or more. In the case study we used Social Economic Survey (Susenasdata collected by BPS. The SAE approach was applied since the Susenas data is not representative enough to estimate the parameters at sub-district level because it’s designed to estimate parameters in regional area (in scope of a district/city at minimum. In this research, the response variable being used was logit function trasformation of pi (the parameter of Binomial distribution. We applied direct and indirect approach for parameter estimation, both using Empirical Bayes approach. For direct estimation we used prior distribution of Beta distribution and Normal prior distribution for logit function (pi and to estimate parameter by using numerical method, i.e integration Monte Carlo. For indirect approach, we used auxiliary variables which are combinations of sex and age (which is divided into five categories. Penalized Quasi Likelihood (PQL was used to get parameter estimation of SAE model and Restricted Maximum Likelihood method (REML for MSE estimation. Instead of Bayesian approach, we are also conducting direct estimation using classical approach in order to evaluate the quality of the estimators. This research gives some findings, those are: Bayesian approach for SAE model gives the best estimation because having the lowest MSE value compares to the other methods. For the direct estimation, Bayesian approach using Beta and logit Normal prior distribution give a very similar result to the direct estimation with classical approach since the weight of is too
Model Diagnostics for Bayesian Networks
Sinharay, Sandip
2006-01-01
Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…
Maximum-Entropy Method for Evaluating the Slope Stability of Earth Dams
Directory of Open Access Journals (Sweden)
Shuai Wang
2012-10-01
Full Text Available The slope stability is a very important problem in geotechnical engineering. This paper presents an approach for slope reliability analysis based on the maximum-entropy method. The key idea is to implement the maximum entropy principle in estimating the probability density function. The performance function is formulated by the Simplified Bishop’s method to estimate the slope failure probability. The maximum-entropy method is used to estimate the probability density function (PDF of the performance function subject to the moment constraints. A numerical example is calculated and compared to the Monte Carlo simulation (MCS and the Advanced First Order Second Moment Method (AFOSM. The results show the accuracy and efficiency of the proposed method. The proposed method should be valuable for performing probabilistic analyses.
Bayesian method for the analysis of the dust emission in the Far-Infrared and Submillimeter
Veneziani, M; Noriega-Crespo, A; Carey, S; Paladini, R; Paradis, D
2013-01-01
We present a method, based on Bayesian statistics, to fit the dust emission parameters in the far-infrared and submillimeter wavelengths. The method estimates the dust temperature and spectral emissivity index, plus their relationship, taking into account properly the statistical and systematic uncertainties. We test it on three sets of simulated sources detectable by the Herschel Space Observatory in the PACS and SPIRE spectral bands (70-500 micron), spanning over a wide range of dust temperatures. The simulated observations are a one-component Interstellar Medium, and two two-component sources, both warm (HII regions) and cold (cold clumps). We first define a procedure to identify the better model, then we recover the parameters of the model and measure their physical correlations by means of a Monte Carlo Markov Chain algorithm adopting multi-variate Gaussian priors. In this process we assess the reliability of the model recovery, and of parameters estimation. We conclude that the model and parameters are ...
Takamizawa, Hisashi; Itoh, Hiroto; Nishiyama, Yutaka
2016-10-01
In order to understand neutron irradiation embrittlement in high fluence regions, statistical analysis using the Bayesian nonparametric (BNP) method was performed for the Japanese surveillance and material test reactor irradiation database. The BNP method is essentially expressed as an infinite summation of normal distributions, with input data being subdivided into clusters with identical statistical parameters, such as mean and standard deviation, for each cluster to estimate shifts in ductile-to-brittle transition temperature (DBTT). The clusters typically depend on chemical compositions, irradiation conditions, and the irradiation embrittlement. Specific variables contributing to the irradiation embrittlement include the content of Cu, Ni, P, Si, and Mn in the pressure vessel steels, neutron flux, neutron fluence, and irradiation temperatures. It was found that the measured shifts of DBTT correlated well with the calculated ones. Data associated with the same materials were subdivided into the same clusters even if neutron fluences were increased.
Singh, Gurmeet; Raj, Ashish; Kressler, Bryan; Nguyen, Thanh D.; Spincemaille, Pascal; Zabih, Ramin; Wang, Yi
2010-01-01
Among recent parallel MR imaging reconstruction advances, a Bayesian method called Edge-preserving Parallel Imaging with GRAph cut Minimization (EPIGRAM) has been demonstrated to significantly improve signal to noise ratio (SNR) compared to conventional regularized sensitivity encoding (SENSE) method. However, EPIGRAM requires a large number of iterations in proportion to the number of intensity labels in the image, making it computationally expensive for high dynamic range images. The objective of this study is to develop a Fast EPIGRAM reconstruction based on the efficient binary jump move algorithm that provides a logarithmic reduction in reconstruction time while maintaining image quality. Preliminary in vivo validation of the proposed algorithm is presented for 2D cardiac cine MR imaging and 3D coronary MR angiography at acceleration factors of 2-4. Fast EPIGRAM was found to provide similar image quality to EPIGRAM and maintain the previously reported SNR improvement over regularized SENSE, while reducing EPIGRAM reconstruction time by 25-50 times. PMID:20939095
Xu, Zhiqiang
2017-02-16
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Bayesian approach to decompression sickness model parameter estimation.
Howle, L E; Weber, P W; Nichols, J M
2017-03-01
We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.
A Hybrid Maximum Power Point Tracking Method for Automobile Exhaust Thermoelectric Generator
Quan, Rui; Zhou, Wei; Yang, Guangyou; Quan, Shuhai
2016-08-01
To make full use of the maximum output power of automobile exhaust thermoelectric generator (AETEG) based on Bi2Te3 thermoelectric modules (TEMs), taking into account the advantages and disadvantages of existing maximum power point tracking methods, and according to the output characteristics of TEMs, a hybrid maximum power point tracking method combining perturb and observe (P&O) algorithm, quadratic interpolation and constant voltage tracking method was put forward in this paper. Firstly, it searched the maximum power point with P&O algorithms and a quadratic interpolation method, then, it forced the AETEG to work at its maximum power point with constant voltage tracking. A synchronous buck converter and controller were implemented in the electric bus of the AETEG applied in a military sports utility vehicle, and the whole system was modeled and simulated with a MATLAB/Simulink environment. Simulation results demonstrate that the maximum output power of the AETEG based on the proposed hybrid method is increased by about 3.0% and 3.7% compared with that using only the P&O algorithm and the quadratic interpolation method, respectively. The shorter tracking time is only 1.4 s, which is reduced by half compared with that of the P&O algorithm and quadratic interpolation method, respectively. The experimental results demonstrate that the tracked maximum power is approximately equal to the real value using the proposed hybrid method,and it can preferentially deal with the voltage fluctuation of the AETEG with only P&O algorithm, and resolve the issue that its working point can barely be adjusted only with constant voltage tracking when the operation conditions change.
A Hybrid Maximum Power Point Tracking Method for Automobile Exhaust Thermoelectric Generator
Quan, Rui; Zhou, Wei; Yang, Guangyou; Quan, Shuhai
2017-05-01
To make full use of the maximum output power of automobile exhaust thermoelectric generator (AETEG) based on Bi2Te3 thermoelectric modules (TEMs), taking into account the advantages and disadvantages of existing maximum power point tracking methods, and according to the output characteristics of TEMs, a hybrid maximum power point tracking method combining perturb and observe (P&O) algorithm, quadratic interpolation and constant voltage tracking method was put forward in this paper. Firstly, it searched the maximum power point with P&O algorithms and a quadratic interpolation method, then, it forced the AETEG to work at its maximum power point with constant voltage tracking. A synchronous buck converter and controller were implemented in the electric bus of the AETEG applied in a military sports utility vehicle, and the whole system was modeled and simulated with a MATLAB/Simulink environment. Simulation results demonstrate that the maximum output power of the AETEG based on the proposed hybrid method is increased by about 3.0% and 3.7% compared with that using only the P&O algorithm and the quadratic interpolation method, respectively. The shorter tracking time is only 1.4 s, which is reduced by half compared with that of the P&O algorithm and quadratic interpolation method, respectively. The experimental results demonstrate that the tracked maximum power is approximately equal to the real value using the proposed hybrid method,and it can preferentially deal with the voltage fluctuation of the AETEG with only P&O algorithm, and resolve the issue that its working point can barely be adjusted only with constant voltage tracking when the operation conditions change.
A method to predict amplitude and date of maximum sunspot number
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A method to predict the amplitude and date of the maximum sunspot number is introduced. The regression analysis of the relationship between the variation rate of monthly sunspot numbers in the initial stage of solar cycles and both of the maximum and the time-length of ascending period of the cycle showed that they are closely correlative. In general, the maximum will be larger and the ascending period will be shorter when the rate is larger. The rate of sunspot numbers in the initial 2 years of the 23rd cycle is thus analyzed based on these grounds and the maximum of the cycle is predicted. For the smoothed monthly sunspot numbers, the maximum will be about 139.2±18.8 and the time-length of ascending period will be about 3.31±0.42 years, that is to say, the maximum will appear around the spring of the year 2000. For the mean monthly ones, the maximum will be near 170.1±22.9 and the time-length of ascending period will be about 3.42±0.46 years, that is to say, the appearing date of the maximum will be later.
Wöhling, Thomas; Geiges, Andreas; Gosses, Moritz; Nowak, Wolfgang
2015-04-01
Data acquisition for monitoring the state in different compartments of complex, coupled environmental systems is often time consuming and expensive. Therefore, experimental monitoring strategies are ideally designed such that most can be learned about the system at minimal costs. Bayesian methods for uncertainty quantification and optimal design (OD) of monitoring strategies are well suited to handle the non-linearity exhibited by most coupled environmental systems. However, their high computational demand restricts their applicability to models with comparatively low run-times. Therefore, pragmatic approaches have been used predominantly in the past where data worth and OD analyses have been restricted to linear or linearised problems and methods. Bayesian (nonlinear) and pragmatic (linear) OD approaches are founded on different assumptions and typically follow different steps in the modelling chain of 1) model calibration, 2) uncertainty quantification, and 3) optimal design analysis. The goal of this study is to follow through these steps for a Bayesian and a pragmatic approach and to discuss the impact of different assumptions (prior uncertainty), calibration strategies, and OD analysis methods on the proposed monitoring designs and their reliability to reduce predictive uncertainty. The OD framework PreDIA (Leube et al. 2012) is used for the nonlinear assessment with a conditional model ensemble obtained with Markov-chain Monte Carlo simulation representing the initial predictive uncertainty. PreDIA can consider any kind of uncertainties and non-linear (statistical) dependencies in data, models, parameters and system drivers during the OD process. In the pragmatic OD approach, the parameter calibration was performed with a non-linear global search and the initial predictive uncertainty was estimated using the PREDUNC utility (Moore and Doherty 2005) of PEST. PREDUNC was also used for the linear OD analysis. We applied PreDIA and PREDUNC for uncertainty
Suspected pulmonary embolism and lung scan interpretation: trial of a Bayesian reporting method.
Becker, D M; Philbrick, J T; Schoonover, F W; Teates, C D
1990-01-01
To determine whether a Bayesian method of lung scan (LS) reporting could influence the management of patients with suspected pulmonary embolism (PE). 1) A descriptive study of the diagnostic process for suspected PE using the new reporting method; 2) a non-experimental evaluation of the reporting method comparing prospective patients and historical controls; and 3) a survey of physicians' reactions to the reporting innovation. University of Virginia Hospital. Of 148 consecutive patients enrolled at the time of LS, 129 were completely evaluated; 75 patients scanned the previous year served as controls. The LS results of patients with suspected PE were reported as posttest probabilities of PE calculated from physician-provided pretest probabilities and the likelihood ratios for PE of LS interpretations. Despite the Bayesian intervention, the confirmation or exclusion of PE was often based on inconclusive evidence. PE was considered by the clinician to be ruled out in 98% of patients with posttest probabilities less than 25% and ruled in for 95% of patients with posttest probabilities greater than 75%. Prospective patients and historical controls were similar in terms of tests ordered after the LS (e.g., pulmonary angiography). Patients with intermediate or indeterminate lung scan results had the highest proportion of subsequent testing. Most physicians (80%) found the reporting innovation to be helpful, either because it confirmed clinical judgement (94 cases) or because it led to additional testing (7 cases). Despite the probabilistic guidance provided by the study, the diagnosis of PE was often neither clearly established nor excluded. While physicians appreciated the innovation and were not confused by the terminology, their clinical decision making was not clearly enhanced.
A computer program for uncertainty analysis integrating regression and Bayesian methods
Lu, Dan; Ye, Ming; Hill, Mary C.; Poeter, Eileen P.; Curtis, Gary
2014-01-01
This work develops a new functionality in UCODE_2014 to evaluate Bayesian credible intervals using the Markov Chain Monte Carlo (MCMC) method. The MCMC capability in UCODE_2014 is based on the FORTRAN version of the differential evolution adaptive Metropolis (DREAM) algorithm of Vrugt et al. (2009), which estimates the posterior probability density function of model parameters in high-dimensional and multimodal sampling problems. The UCODE MCMC capability provides eleven prior probability distributions and three ways to initialize the sampling process. It evaluates parametric and predictive uncertainties and it has parallel computing capability based on multiple chains to accelerate the sampling process. This paper tests and demonstrates the MCMC capability using a 10-dimensional multimodal mathematical function, a 100-dimensional Gaussian function, and a groundwater reactive transport model. The use of the MCMC capability is made straightforward and flexible by adopting the JUPITER API protocol. With the new MCMC capability, UCODE_2014 can be used to calculate three types of uncertainty intervals, which all can account for prior information: (1) linear confidence intervals which require linearity and Gaussian error assumptions and typically 10s–100s of highly parallelizable model runs after optimization, (2) nonlinear confidence intervals which require a smooth objective function surface and Gaussian observation error assumptions and typically 100s–1,000s of partially parallelizable model runs after optimization, and (3) MCMC Bayesian credible intervals which require few assumptions and commonly 10,000s–100,000s or more partially parallelizable model runs. Ready access allows users to select methods best suited to their work, and to compare methods in many circumstances.
A viable method for goodness-of-fit test in maximum likelihood fit
Institute of Scientific and Technical Information of China (English)
ZHANG Feng; GAO Yuan-Ning; HUO Lei
2011-01-01
A test statistic is proposed to perform the goodness-of-fit test in the unbinned maximum likelihood fit. Without using a detailed expression of the efficiency function, the test statistic is found to be strongly correlated with the maximum likelihood function if the efficiency function varies smoothly. We point out that the correlation coefficient can be estimated by the Monte Carlo technique. With the established method, two examples are given to illustrate the performance of the test statistic.
Fang, W.; Quan, S. H.; Xie, C. J.; Tang, X. F.; Wang, L. L.; Huang, L.
2016-03-01
In this study, a direct-current/direct-current (DC/DC) converter with maximum power point tracking (MPPT) is developed to down-convert the high voltage DC output from a thermoelectric generator to the lower voltage required to charge batteries. To improve the tracking accuracy and speed of the converter, a novel MPPT control scheme characterized by an aggregated dichotomy and gradient (ADG) method is proposed. In the first stage, the dichotomy algorithm is used as a fast search method to find the approximate region of the maximum power point. The gradient method is then applied for rapid and accurate tracking of the maximum power point. To validate the proposed MPPT method, a test bench composed of an automobile exhaust thermoelectric generator was constructed for harvesting the automotive exhaust heat energy. Steady-state and transient tracking experiments under five different load conditions were carried out using a DC/DC converter with the proposed ADG and with three traditional methods. The experimental results show that the ADG method can track the maximum power within 140 ms with a 1.1% error rate when the engine operates at 3300 rpm@71 NM, which is superior to the performance of the single dichotomy method, the single gradient method and the perturbation and observation method from the viewpoint of improved tracking accuracy and speed.
Institute of Scientific and Technical Information of China (English)
王雪丽; 陶剑; 史宁中
2005-01-01
The primary goal of a phase I clinical trial is to find the maximum tolerable dose of a treatment. In this paper, we propose a new stepwise method based on confidence bound and information incorporation to determine the maximum tolerable dose among given dose levels. On the one hand, in order to avoid severe even fatal toxicity to occur and reduce the experimental subjects, the new method is executed from the lowest dose level, and then goes on in a stepwise fashion. On the other hand,in order to improve the accuracy of the recommendation, the final recommendation of the maximum tolerable dose is accomplished through the information incorporation of an additional experimental cohort at the same dose level. Furthermore, empirical simulation results show that the new method has some real advantages in comparison with the modified continual reassessment method.
Bayesian Inference for LISA Pathfinder using Markov Chain Monte Carlo Methods
Ferraioli, Luigi; Plagnol, Eric
2012-01-01
We present a parameter estimation procedure based on a Bayesian framework by applying a Markov Chain Monte Carlo algorithm to the calibration of the dynamical parameters of a space based gravitational wave detector. The method is based on the Metropolis-Hastings algorithm and a two-stage annealing treatment in order to ensure an effective exploration of the parameter space at the beginning of the chain. We compare two versions of the algorithm with an application to a LISA Pathfinder data analysis problem. The two algorithms share the same heating strategy but with one moving in coordinate directions using proposals from a multivariate Gaussian distribution, while the other uses the natural logarithm of some parameters and proposes jumps in the eigen-space of the Fisher Information matrix. The algorithm proposing jumps in the eigen-space of the Fisher Information matrix demonstrates a higher acceptance rate and a slightly better convergence towards the equilibrium parameter distributions in the application to...
DEFF Research Database (Denmark)
2010-01-01
Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context...... of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable....... This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides...
Bayesian methods for model uncertainty analysis with application to future sea level rise
Energy Technology Data Exchange (ETDEWEB)
Patwardhan, A.; Small, M.J.
1992-01-01
In no other area is the need for effective analysis of uncertainty more evident than in the problem of evaluating the consequences of increasing atmospheric concentrations of radiatively active gases. The major consequences of concern is global warming, with related environmental effects that include changes in local patterns of precipitation, soil moisture, forest and agricultural productivity, and a potential increase in global mean sea level. In order to identify an optimum set of responses to sea level change, a full characterization of the uncertainties associated with the predictions of future sea level rise is essential. The paper addresses the use of data for identifying and characterizing uncertainties in model parameters and predictions. The Bayesian Monte Carlo method is formally presented and elaborated, and applied to the analysis of the uncertainty in a predictive model for global mean sea level change.
Bayesian analysis of general failure data from an ageing distribution: advances in numerical methods
Energy Technology Data Exchange (ETDEWEB)
Procaccia, H.; Villain, B. [Electricite de France (EDF), 93 - Saint-Denis (France); Clarotti, C.A. [ENEA, Casaccia (Italy)
1996-12-31
EDF and ENEA carried out a joint research program for developing the numerical methods and computer codes needed for Bayesian analysis of component-lives in the case of ageing. Early results of this study were presented at ESREL`94. Since then the following further steps have been gone: input data have been generalized to the case that observed lives are censored both on the right and on the left; allowable life distributions are Weibull and gamma - their parameters are both unknown and can be statistically dependent; allowable priors are histograms relative to different parametrizations of the life distribution of concern; first-and-second-order-moments of the posterior distributions can be computed. In particular the covariance will give some important information about the degree of the statistical dependence between the parameters of interest. An application of the code to the appearance of a stress corrosion cracking in a tube of the PWR Steam Generator system is presented. (authors). 10 refs.
Physics-based, Bayesian sequential detection method and system for radioactive contraband
Candy, James V; Axelrod, Michael C; Breitfeller, Eric F; Chambers, David H; Guidry, Brian L; Manatt, Douglas R; Meyer, Alan W; Sale, Kenneth E
2014-03-18
A distributed sequential method and system for detecting and identifying radioactive contraband from highly uncertain (noisy) low-count, radionuclide measurements, i.e. an event mode sequence (EMS), using a statistical approach based on Bayesian inference and physics-model-based signal processing based on the representation of a radionuclide as a monoenergetic decomposition of monoenergetic sources. For a given photon event of the EMS, the appropriate monoenergy processing channel is determined using a confidence interval condition-based discriminator for the energy amplitude and interarrival time and parameter estimates are used to update a measured probability density function estimate for a target radionuclide. A sequential likelihood ratio test is then used to determine one of two threshold conditions signifying that the EMS is either identified as the target radionuclide or not, and if not, then repeating the process for the next sequential photon event of the EMS until one of the two threshold conditions is satisfied.
King, Benedict; Qiao, Tuo; Lee, Michael S Y; Zhu, Min; Long, John A
2017-07-01
The phylogeny of early gnathostomes provides an important framework for understanding one of the most significant evolutionary events, the origin and diversification of jawed vertebrates. A series of recent cladistic analyses have suggested that the placoderms, an extinct group of armoured fish, form a paraphyletic group basal to all other jawed vertebrates. We revised and expanded this morphological data set, most notably by sampling autapomorphies in a similar way to parsimony-informative traits, thus ensuring this data (unlike most existing morphological data sets) satisfied an important assumption of Bayesian tip-dated morphological clock approaches. We also found problems with characters supporting placoderm paraphyly, including character correlation and incorrect codings. Analysis of this data set reveals that paraphyly and monophyly of core placoderms (excluding maxillate forms) are essentially equally parsimonious. The two alternative topologies have different root positions for the jawed vertebrates but are otherwise similar. However, analysis using tip-dated clock methods reveals strong support for placoderm monophyly, due to this analysis favoring trees with more balanced rates of evolution. Furthermore, enforcing placoderm paraphyly results in higher levels and unusual patterns of rate heterogeneity among branches, similar to that generated from simulated trees reconstructed with incorrect root positions. These simulations also show that Bayesian tip-dated clock methods outperform parsimony when the outgroup is largely uninformative (e.g., due to inapplicable characters), as might be the case here. The analysis also reveals that gnathostomes underwent a rapid burst of evolution during the Silurian period which declined during the Early Devonian. This rapid evolution during a period with few articulated fossils might partly explain the difficulty in ascertaining the root position of jawed vertebrates. © The Author(s) 2016. Published by Oxford University
Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring
Huff, Daniel W.
Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time
A Maximum Likelihood Method for Latent Class Regression Involving a Censored Dependent Variable.
Jedidi, Kamel; And Others
1993-01-01
A method is proposed to simultaneously estimate regression functions and subject membership in "k" latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The method is illustrated through a consumer psychology application. (SLD)
Magnard, Christophe; Small, David; Meier, Erich
2015-01-01
The phase estimation of cross-track multibaseline synthetic aperture interferometric data is usually thought to be very efficiently achieved using the maximum likelihood (ML) method. The suitability of this method is investigated here as applied to airborne single pass multibaseline data. Experimental interferometric data acquired with a Ka-band sensor were processed using (a) a ML method that fuses the complex data from all receivers and (b) a coarse-to-fine method that only uses the interme...
Lee, Sik-Yum; Song, Xin-Yuan; Tang, Nian-Sheng
2007-01-01
The analysis of interaction among latent variables has received much attention. This article introduces a Bayesian approach to analyze a general structural equation model that accommodates the general nonlinear terms of latent variables and covariates. This approach produces a Bayesian estimate that has the same statistical optimal properties as a…
A Hybrid Optimization Method for Solving Bayesian Inverse Problems under Uncertainty.
Directory of Open Access Journals (Sweden)
Kai Zhang
Full Text Available In this paper, we investigate the application of a new method, the Finite Difference and Stochastic Gradient (Hybrid method, for history matching in reservoir models. History matching is one of the processes of solving an inverse problem by calibrating reservoir models to dynamic behaviour of the reservoir in which an objective function is formulated based on a Bayesian approach for optimization. The goal of history matching is to identify the minimum value of an objective function that expresses the misfit between the predicted and measured data of a reservoir. To address the optimization problem, we present a novel application using a combination of the stochastic gradient and finite difference methods for solving inverse problems. The optimization is constrained by a linear equation that contains the reservoir parameters. We reformulate the reservoir model's parameters and dynamic data by operating the objective function, the approximate gradient of which can guarantee convergence. At each iteration step, we obtain the relatively 'important' elements of the gradient, which are subsequently substituted by the values from the Finite Difference method through comparing the magnitude of the components of the stochastic gradient, which forms a new gradient, and we subsequently iterate with the new gradient. Through the application of the Hybrid method, we efficiently and accurately optimize the objective function. We present a number numerical simulations in this paper that show that the method is accurate and computationally efficient.
A Hybrid Optimization Method for Solving Bayesian Inverse Problems under Uncertainty.
Zhang, Kai; Wang, Zengfei; Zhang, Liming; Yao, Jun; Yan, Xia
2015-01-01
In this paper, we investigate the application of a new method, the Finite Difference and Stochastic Gradient (Hybrid method), for history matching in reservoir models. History matching is one of the processes of solving an inverse problem by calibrating reservoir models to dynamic behaviour of the reservoir in which an objective function is formulated based on a Bayesian approach for optimization. The goal of history matching is to identify the minimum value of an objective function that expresses the misfit between the predicted and measured data of a reservoir. To address the optimization problem, we present a novel application using a combination of the stochastic gradient and finite difference methods for solving inverse problems. The optimization is constrained by a linear equation that contains the reservoir parameters. We reformulate the reservoir model's parameters and dynamic data by operating the objective function, the approximate gradient of which can guarantee convergence. At each iteration step, we obtain the relatively 'important' elements of the gradient, which are subsequently substituted by the values from the Finite Difference method through comparing the magnitude of the components of the stochastic gradient, which forms a new gradient, and we subsequently iterate with the new gradient. Through the application of the Hybrid method, we efficiently and accurately optimize the objective function. We present a number numerical simulations in this paper that show that the method is accurate and computationally efficient.
Locating disease genes using Bayesian variable selection with the Haseman-Elston method
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He Qimei
2003-12-01
Full Text Available Abstract Background We applied stochastic search variable selection (SSVS, a Bayesian model selection method, to the simulated data of Genetic Analysis Workshop 13. We used SSVS with the revisited Haseman-Elston method to find the markers linked to the loci determining change in cholesterol over time. To study gene-gene interaction (epistasis and gene-environment interaction, we adopted prior structures, which incorporate the relationship among the predictors. This allows SSVS to search in the model space more efficiently and avoid the less likely models. Results In applying SSVS, instead of looking at the posterior distribution of each of the candidate models, which is sensitive to the setting of the prior, we ranked the candidate variables (markers according to their marginal posterior probability, which was shown to be more robust to the prior. Compared with traditional methods that consider one marker at a time, our method considers all markers simultaneously and obtains more favorable results. Conclusions We showed that SSVS is a powerful method for identifying linked markers using the Haseman-Elston method, even for weak effects. SSVS is very effective because it does a smart search over the entire model space.
On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method.
Roux, Benoît; Weare, Jonathan
2013-02-28
An issue of general interest in computer simulations is to incorporate information from experiments into a structural model. An important caveat in pursuing this goal is to avoid corrupting the resulting model with spurious and arbitrary biases. While the problem of biasing thermodynamic ensembles can be formulated rigorously using the maximum entropy method introduced by Jaynes, the approach can be cumbersome in practical applications with the need to determine multiple unknown coefficients iteratively. A popular alternative strategy to incorporate the information from experiments is to rely on restrained-ensemble molecular dynamics simulations. However, the fundamental validity of this computational strategy remains in question. Here, it is demonstrated that the statistical distribution produced by restrained-ensemble simulations is formally consistent with the maximum entropy method of Jaynes. This clarifies the underlying conditions under which restrained-ensemble simulations will yield results that are consistent with the maximum entropy method.
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F. S. Zhang
2016-01-01
Full Text Available The spatial mapping of losses attributable to such disasters is now well established as a means of describing the spatial patterns of disaster risk, and it has been shown to be suitable for many types of major meteorological disasters. However, few studies have been carried out by developing a regression model to estimate the effects of the spatial distribution of meteorological factors on losses associated with meteorological disasters. In this study, the proposed approach is capable of the following: (a estimating the spatial distributions of seven meteorological factors using Bayesian maximum entropy, (b identifying the four mapping methods used in this research with the best performance based on the cross validation, and (c establishing a fitted model between the PLS components and disaster losses information using partial least squares regression within a specific research area. The results showed the following: (a best mapping results were produced by multivariate Bayesian maximum entropy with probabilistic soft data; (b the regression model using three PLS components, extracted from seven meteorological factors by PLS method, was the most predictive by means of PRESS/SS test; (c northern Hunan Province sustains the most damage, and southeastern Gansu Province and western Guizhou Province sustained the least.
Liu, Y. R.; Li, Y. P.; Huang, G. H.; Zhang, J. L.; Fan, Y. R.
2017-10-01
In this study, a Bayesian-based multilevel factorial analysis (BMFA) method is developed to assess parameter uncertainties and their effects on hydrological model responses. In BMFA, Differential Evolution Adaptive Metropolis (DREAM) algorithm is employed to approximate the posterior distributions of model parameters with Bayesian inference; factorial analysis (FA) technique is used for measuring the specific variations of hydrological responses in terms of posterior distributions to investigate the individual and interactive effects of parameters on model outputs. BMFA is then applied to a case study of the Jinghe River watershed in the Loess Plateau of China to display its validity and applicability. The uncertainties of four sensitive parameters, including soil conservation service runoff curve number to moisture condition II (CN2), soil hydraulic conductivity (SOL_K), plant available water capacity (SOL_AWC), and soil depth (SOL_Z), are investigated. Results reveal that (i) CN2 has positive effect on peak flow, implying that the concentrated rainfall during rainy season can cause infiltration-excess surface flow, which is an considerable contributor to peak flow in this watershed; (ii) SOL_K has positive effect on average flow, implying that the widely distributed cambisols can lead to medium percolation capacity; (iii) the interaction between SOL_AWC and SOL_Z has noticeable effect on the peak flow and their effects are dependent upon each other, which discloses that soil depth can significant influence the processes of plant uptake of soil water in this watershed. Based on the above findings, the significant parameters and the relationship among uncertain parameters can be specified, such that hydrological model's capability for simulating/predicting water resources of the Jinghe River watershed can be improved.
Bayesian data analysis for newcomers.
Kruschke, John K; Liddell, Torrin M
2017-04-12
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.
Directory of Open Access Journals (Sweden)
Fuqiang Sun
2017-01-01
Full Text Available Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.
Energy Technology Data Exchange (ETDEWEB)
Kawaguchi, K.; Egashira, Y.; Watanabe, G. [Mazda Motor Corp., Hiroshima (Japan)
1997-10-01
Vehicle and unit performance change according to not only external causes represented by the environment such as temperature or weather, but also internal causes which are dispersion of component characteristics and manufacturing processes or aged deteriorations. We developed the design method to estimate thus performance distributions with maximum entropy method and to calculate specifications with high performance robustness using Fuzzy theory. This paper describes the details of these methods and examples applied to power window system. 3 refs., 7 figs., 4 tabs.
Application of the maximum relative entropy method to the physics of ferromagnetic materials
Giffin, Adom; Cafaro, Carlo; Ali, Sean Alan
2016-08-01
It is known that the Maximum relative Entropy (MrE) method can be used to both update and approximate probability distributions functions in statistical inference problems. In this manuscript, we apply the MrE method to infer magnetic properties of ferromagnetic materials. In addition to comparing our approach to more traditional methodologies based upon the Ising model and Mean Field Theory, we also test the effectiveness of the MrE method on conventionally unexplored ferromagnetic materials with defects.
Magnard, C.; Small, D.; Meier, E.
2015-03-01
The phase estimation of cross-track multibaseline synthetic aperture interferometric data is usually thought to be very efficiently achieved using the maximum likelihood (ML) method. The suitability of this method is investigated here as applied to airborne single pass multibaseline data. Experimental interferometric data acquired with a Ka-band sensor were processed using (a) a ML method that fuses the complex data from all receivers and (b) a coarse-to-fine method that only uses the intermediate baselines to unwrap the phase values from the longest baseline. The phase noise was analyzed for both methods: in most cases, a small improvement was found when the ML method was used.
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Limin Liao
2016-05-01
Full Text Available Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM, and the Flexible Spatiotemporal DAta Fusion (FSDAF method showed that our proposed method has the following advantages: (1 it can obtain more accurate estimates; (2 it can retain more spatial detail; (3 its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4 it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies.
Self-Organizing Genetic Algorithm Based Method for Constructing Bayesian Networks from Databases
Institute of Scientific and Technical Information of China (English)
郑建军; 刘玉树; 陈立潮
2003-01-01
The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.
DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning
Soufan, Othman
2016-11-10
Background Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, methods and solutions proposed to-date, did not completely overcome these challenges. This study is based on a multi-label classification (MLC) technique for modeling correlations between several HTS assays, meaning that a single prediction represents a subset of assigned correlated labels instead of one label. Thus, the devised method provides an increased probability for more accurate predictions of compounds that were not tested in particular assays. Results Here we present DRABAL, a novel MLC solution that incorporates structure learning of a Bayesian network as a step to model dependency between the HTS assays. In this study, DRABAL was used to process more than 1.4 million interactions of over 400,000 compounds and analyze the existing relationships between five large HTS assays from the PubChem BioAssay Database. Compared to different MLC methods, DRABAL significantly improves the F1Score by about 22%, on average. We further illustrated usefulness and utility of DRABAL through screening FDA approved drugs and reported ones that have a high probability to interact with several targets, thus enabling drug-multi-target repositioning. Specifically DRABAL suggests the Thiabendazole drug as a common activator of the NCP1 and Rab-9A proteins, both of which are designed to identify treatment modalities for the Niemann–Pick type C disease. Conclusion We developed a novel MLC solution based on a Bayesian active learning framework to overcome the challenge of lacking fully labeled training data and exploit actual dependencies between the HTS assays. The solution is motivated by the need to model dependencies between existing
Dorn, Caroline; Venturini, Julia; Khan, Amir; Heng, Kevin; Alibert, Yann; Helled, Ravit; Rivoldini, Attilio; Benz, Willy
2017-01-01
Aims: We aim to present a generalized Bayesian inference method for constraining interiors of super Earths and sub-Neptunes. Our methodology succeeds in quantifying the degeneracy and correlation of structural parameters for high dimensional parameter spaces. Specifically, we identify what constraints can be placed on composition and thickness of core, mantle, ice, ocean, and atmospheric layers given observations of mass, radius, and bulk refractory abundance constraints (Fe, Mg, Si) from observations of the host star's photospheric composition. Methods: We employed a full probabilistic Bayesian inference analysis that formally accounts for observational and model uncertainties. Using a Markov chain Monte Carlo technique, we computed joint and marginal posterior probability distributions for all structural parameters of interest. We included state-of-the-art structural models based on self-consistent thermodynamics of core, mantle, high-pressure ice, and liquid water. Furthermore, we tested and compared two different atmospheric models that are tailored for modeling thick and thin atmospheres, respectively. Results: First, we validate our method against Neptune. Second, we apply it to synthetic exoplanets of fixed mass and determine the effect on interior structure and composition when (1) radius; (2) atmospheric model; (3) data uncertainties; (4) semi-major axes; (5) atmospheric composition (i.e., a priori assumption of enriched envelopes versus pure H/He envelopes); and (6) prior distributions are varied. Conclusions: Our main conclusions are: (1) given available data, the range of possible interior structures is large; quantification of the degeneracy of possible interiors is therefore indispensable for meaningful planet characterization. (2) Our method predicts models that agree with independent estimates of Neptune's interior. (3) Increasing the precision in mass and radius leads to much improved constraints on ice mass fraction, size of rocky interior, but
Liao, Stephen Shaoyi; Wang, Huai Qing; Li, Qiu Dan; Liu, Wei Yi
2006-06-01
This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.
Valuing option on the maximum of two assets using improving modified Gauss-Seidel method
Koh, Wei Sin; Muthuvalu, Mohana Sundaram; Aruchunan, Elayaraja; Sulaiman, Jumat
2014-07-01
This paper presents the numerical solution for the option on the maximum of two assets using Improving Modified Gauss-Seidel (IMGS) iterative method. Actually, this option can be governed by two-dimensional Black-Scholes partial differential equation (PDE). The Crank-Nicolson scheme is applied to discretize the Black-Scholes PDE in order to derive a linear system. Then, the IMGS iterative method is formulated to solve the linear system. Numerical experiments involving Gauss-Seidel (GS) and Modified Gauss-Seidel (MGS) iterative methods are implemented as control methods to test the computational efficiency of the IMGS iterative method.
Russo, T. A.; Devineni, N.; Lall, U.
2015-12-01
Lasting success of the Green Revolution in Punjab, India relies on continued availability of local water resources. Supplying primarily rice and wheat for the rest of India, Punjab supports crop irrigation with a canal system and groundwater, which is vastly over-exploited. The detailed data required to physically model future impacts on water supplies agricultural production is not readily available for this region, therefore we use Bayesian methods to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. Using measured values of historical precipitation, total canal water delivery, crop yield, and water table elevation, we present a method using a Markov chain Monte Carlo (MCMC) algorithm to solve for a distribution of values for each unknown parameter in a conceptual mass balance model. Due to heterogeneity across the state, and the resolution of input data, we estimate model parameters at the district-scale using spatial pooling. The resulting model is used to predict the impact of precipitation change scenarios on groundwater availability under multiple cropping options. Predicted groundwater declines vary across the state, suggesting that crop selection and water management strategies should be determined at a local scale. This computational method can be applied in data-scarce regions across the world, where water resource management is required to resolve competition between food security and available resources in a changing climate.
Institute of Scientific and Technical Information of China (English)
阚英男; 杨兆军; 李国发; 何佳龙; 王彦鹍; 李洪洲
2016-01-01
A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools (NCMTs) reveal zero failures after a reliability test. Thus, the zero-failure data form and corresponding Bayesian model are developed to solve the zero-failure problem of NCMTs, for which no previous suitable statistical model has been developed. An expert−judgment process that incorporates prior information is presented to solve the difficulty in obtaining reliable prior distributions of Weibull parameters. The equations for the posterior distribution of the parameter vector and the Markov chain Monte Carlo (MCMC) algorithm are derived to solve the difficulty of calculating high-dimensional integration and to obtain parameter estimators. The proposed method is applied to a real case; a corresponding programming code and trick are developed to implement an MCMC simulation in WinBUGS, and a mean time between failures (MTBF) of 1057.9 h is obtained. Given its ability to combine expert judgment, prior information, and data, the proposed reliability modeling and assessment method under the zero failure of NCMTs is validated.
A Bayesian Analysis of Spectral ARMA Model
Directory of Open Access Journals (Sweden)
Manoel I. Silvestre Bezerra
2012-01-01
Full Text Available Bezerra et al. (2008 proposed a new method, based on Yule-Walker equations, to estimate the ARMA spectral model. In this paper, a Bayesian approach is developed for this model by using the noninformative prior proposed by Jeffreys (1967. The Bayesian computations, simulation via Markov Monte Carlo (MCMC is carried out and characteristics of marginal posterior distributions such as Bayes estimator and confidence interval for the parameters of the ARMA model are derived. Both methods are also compared with the traditional least squares and maximum likelihood approaches and a numerical illustration with two examples of the ARMA model is presented to evaluate the performance of the procedures.
Baptista, F M; Alban, L; Olsen, A M; Petersen, J V; Toft, N
2012-10-01
Residues of pharmacological active substances or their metabolites might be found in food products from food-producing animals. Maximum Residue Limits for pharmacological active substances in foodstuffs of animal origin are established to assure high food safety standards. Each year, more than 20,000 samples are analysed for the presence of antibacterial residues in Danish pigs. This corresponds to 0.1% of the size of the slaughter pig population and more than 1% of the sows slaughtered. In this study, a Bayesian model was used to evaluate the Danish surveillance system accuracy and to investigate the impact of a potential risk-based sampling approach to the residue surveillance programme in Danish slaughter pigs. Danish surveillance data from 2005 to 2009 and limited knowledge about true prevalence and test sensitivity and specificity were included in the model. According to the model, the true antibacterial residue prevalence in Danish pigs is very low in both sows (∼0.20%) and slaughter pigs (∼0.01%). Despite data constraints, the results suggest that the current screening test used in Denmark presents high sensitivity (85-99%) and very high specificity (>99%) for the most relevant antibacterial classes used in Danish pigs. If high-risk slaughter pigs could be identified by taking into account antibacterial use or meat inspection risk factors, a potential risk-based sampling approach to antibacterial residue surveillance in slaughter pigs would allow reducing the sample size substantially, while increasing or maintaining the probability of detection. Hence, the antibacterial residue surveillance programme in Danish pigs would be more cost-effective than today.
A Modified Levenberg-Marquardt Method for Nonsmooth Equations with Finitely Many Maximum Functions
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Yan Gao
2009-02-01
Full Text Available For solving nonsmooth systems of equations, the Levenberg-Marquardt method and its variants are of particular importance because of their locally fast convergent rates. Finitely many maximum functions systems are very useful in the study of nonlinear complementarity problems, variational inequality problems, Karush-Kuhn-Tucker systems of nonlinear programming problems, and many problems in mechanics and engineering. In this paper, we present a modified Levenberg-Marquardt method for nonsmooth equations with finitely many maximum functions. Under mild assumptions, the present method is shown to be convergent Q-linearly. Some numerical results comparing the proposed method with classical reformulations indicate that the modified Levenberg-Marquardt algorithm works quite well in practice.
Evaluation of the dynamic processes of a landslide with laser scanners and Bayesian methods
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A. Guarnieri
2015-07-01
Full Text Available This paper deals with the study of the dynamics of a landslide from two different but complementary point of views. The landslide is situated within the Miozza basin, an area of approximately 10.7 km2 located in the Alpine region of Carnia (Italy. In the first part of the paper, the macro-scale analysis of volumetric changes occurred after the reactivation of landslide in 2004 is addressed by using a two-epoch laser scanning surveys from airborne (ALS and terrestrial (TLS platforms. airborne laser scanning (ALS data were collected in 2003 (before reactivation of the phenomenon with an ALTM 3033 OPTECH sensor while terrestrial laser scanning (TLS measurements were acquired in 2008 with a Riegl LMS-Z620. The second part of the paper deals with the study of dynamic processes of the landslide at micro-scale. To this aim, a global navigation satellite system (GNSS-based monitoring network is analysed using a statistical approach to discriminate between measurement noise and possible actual displacements. This task is accomplished using both “classical” statistical testing and a Bayesian approach. The second method has been employed to verify some apparent vertical displacements detected by the classical test. As regards the first topic of the paper, achieved results show that long-range TLS instruments can be profitably used in mountain areas to provide high-resolution digital terrain models (DTMs with superior quality and detail with respect to aerial light detection and ranging data only, even in areas with very low accessibility. Moreover, ALS- and TLS-derived DTMs can be combined each other in order to fill gaps in ALS data, mainly due to the complexity of terrain morphology, and to perform quite accurate calculations of volume changes due to landslide phenomenon. Finally, the outcomes of the application of Bayesian inference demonstrate the effectiveness of this method to better detect statistically significant displacements of a GNSS
Estimation of bias errors in measured airplane responses using maximum likelihood method
Klein, Vladiaslav; Morgan, Dan R.
1987-01-01
A maximum likelihood method is used for estimation of unknown bias errors in measured airplane responses. The mathematical model of an airplane is represented by six-degrees-of-freedom kinematic equations. In these equations the input variables are replaced by their measured values which are assumed to be without random errors. The resulting algorithm is verified with a simulation and flight test data. The maximum likelihood estimates from in-flight measured data are compared with those obtained by using a nonlinear-fixed-interval-smoother and an extended Kalmar filter.
A Modified Levenberg-Marquardt Method for Nonsmooth Equations with Finitely Many Maximum Functions
Yan Gao; Shou-qiang Du
2009-01-01
For solving nonsmooth systems of equations, the Levenberg-Marquardt method and its variants are of particular importance because of their locally fast convergent rates. Finitely many maximum functions systems are very useful in the study of nonlinear complementarity problems, variational inequality problems, Karush-Kuhn-Tucker systems of nonlinear programming problems, and many problems in mechanics and engineering. In this paper, we present a modified Levenberg-Marquardt method for nonsmooth...
Bias in diet determination: incorporating traditional methods in Bayesian mixing models.
Franco-Trecu, Valentina; Drago, Massimiliano; Riet-Sapriza, Federico G; Parnell, Andrew; Frau, Rosina; Inchausti, Pablo
2013-01-01
There are not "universal methods" to determine diet composition of predators. Most traditional methods are biased because of their reliance on differential digestibility and the recovery of hard items. By relying on assimilated food, stable isotope and Bayesian mixing models (SIMMs) resolve many biases of traditional methods. SIMMs can incorporate prior information (i.e. proportional diet composition) that may improve the precision in the estimated dietary composition. However few studies have assessed the performance of traditional methods and SIMMs with and without informative priors to study the predators' diets. Here we compare the diet compositions of the South American fur seal and sea lions obtained by scats analysis and by SIMMs-UP (uninformative priors) and assess whether informative priors (SIMMs-IP) from the scat analysis improved the estimated diet composition compared to SIMMs-UP. According to the SIMM-UP, while pelagic species dominated the fur seal's diet the sea lion's did not have a clear dominance of any prey. In contrast, SIMM-IP's diets compositions were dominated by the same preys as in scat analyses. When prior information influenced SIMMs' estimates, incorporating informative priors improved the precision in the estimated diet composition at the risk of inducing biases in the estimates. If preys isotopic data allow discriminating preys' contributions to diets, informative priors should lead to more precise but unbiased estimated diet composition. Just as estimates of diet composition obtained from traditional methods are critically interpreted because of their biases, care must be exercised when interpreting diet composition obtained by SIMMs-IP. The best approach to obtain a near-complete view of predators' diet composition should involve the simultaneous consideration of different sources of partial evidence (traditional methods, SIMM-UP and SIMM-IP) in the light of natural history of the predator species so as to reliably ascertain and
Pursley, Jennifer; Risholm, Petter; Fedorov, Andriy; Tuncali, Kemal; Fennessy, Fiona M.; Wells, William M.; Tempany, Clare M.; Cormack, Robert A.
2012-01-01
Purpose: This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible. Previous efforts to register diagnostic prostate images with the intraoperative coordinate system have been deterministic and did not offer a measure of the registration uncertainty. The authors developed a Bayesian registration method to estimate the posterior distribution on deformations and provide a case-specific measure of the associated registration uncertainty. Methods: The authors adapted a biomechanical-based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician's segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum a posteriori deformation and the associated uncertainty were estimated from the collection of deformation samples drawn from the posterior distribution. The authors validated the registration method using a dataset created from ten patients with MRI-guided prostate biopsies who had both diagnostic and intraprocedural 3 Tesla MRI scans. The accuracy and precision of the estimated posterior distribution on deformations were evaluated from two predictive distance distributions: between the deformed central zone-peripheral zone (CZ-PZ) interface and the physician-labeled interface, and based on physician-defined landmarks. Geometric margins on the registration of the prostate's peripheral zone were determined from the posterior predictive distance to the CZ-PZ interface separately for the base, mid-gland, and apical regions of the prostate. Results: The authors observed
The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis
Directory of Open Access Journals (Sweden)
Chen Yidong
2004-01-01
Full Text Available An unsupervised data clustering method, called the local maximum clustering (LMC method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999.
The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network
Directory of Open Access Journals (Sweden)
Yihua Zhong
2016-01-01
Full Text Available Oilfield development aiming at crude oil production is an extremely complex process, which involves many uncertain risk factors affecting oil output. Thus, risk prediction and early warning about oilfield development may insure operating and managing oilfields efficiently to meet the oil production plan of the country and sustainable development of oilfields. However, scholars and practitioners in the all world are seldom concerned with the risk problem of oilfield block development. The early warning index system of blocks development which includes the monitoring index and planning index was refined and formulated on the basis of researching and analyzing the theory of risk forecasting and early warning as well as the oilfield development. Based on the indexes of warning situation predicted by neural network, the method dividing the interval of warning degrees was presented by “3σ” rule; and a new method about forecasting and early warning of risk was proposed by introducing neural network to Bayesian networks. Case study shows that the results obtained in this paper are right and helpful to the management of oilfield development risk.
A Bayesian Chance-Constrained Method for Hydraulic Barrier Design Under Model Structure Uncertainty
Chitsazan, N.; Pham, H. V.; Tsai, F. T. C.
2014-12-01
The groundwater community has widely recognized the model structure uncertainty as the major source of model uncertainty in groundwater modeling. Previous studies in the aquifer remediation design, however, rarely discuss the impact of the model structure uncertainty. This study combines the chance-constrained (CC) programming with the Bayesian model averaging (BMA) as a BMA-CC framework to assess the effect of model structure uncertainty in the remediation design. To investigate the impact of the model structure uncertainty on the remediation design, we compare the BMA-CC method with the traditional CC programming that only considers the model parameter uncertainty. The BMA-CC method is employed to design a hydraulic barrier to protect public supply wells of the Government St. pump station from saltwater intrusion in the "1,500-foot" sand and the "1-700-foot" sand of the Baton Rouge area, southeastern Louisiana. To address the model structure uncertainty, we develop three conceptual groundwater models based on three different hydrostratigraphy structures. The results show that using the traditional CC programming overestimates design reliability. The results also show that at least five additional connector wells are needed to achieve more than 90% design reliability level. The total amount of injected water from connector wells is higher than the total pumpage of the protected public supply wells. While reducing injection rate can be achieved by reducing reliability level, the study finds that the hydraulic barrier design to protect the Government St. pump station is not economically attractive.
Bijaoui, A.
2013-03-01
The image restoration is today an important part of the astrophysical data analysis. The denoising and the deblurring can be efficiently performed using multiscale transforms. The multiresolution analysis constitutes the fundamental pillar for these transforms. The discrete wavelet transform is introduced from the theory of the approximation by translated functions. The continuous wavelet transform carries out a generalization of multiscale representations from translated and dilated wavelets. The à trous algorithm furnishes its discrete redundant transform. The image denoising is first considered without any hypothesis on the signal distribution, on the basis of the a contrario detection. Different softening functions are introduced. The introduction of a regularization constraint may improve the results. The application of Bayesian methods leads to an automated adaptation of the softening function to the signal distribution. The MAP principle leads to the basis pursuit, a sparse decomposition on redundant dictionaries. Nevertheless the posterior expectation minimizes, scale per scale, the quadratic error. The proposed deconvolution algorithm is based on a coupling of the wavelet denoising with an iterative inversion algorithm. The different methods are illustrated by numerical experiments on a simulated image similar to images of the deep sky. A white Gaussian stationary noise was added with three levels. In the conclusion different important connected problems are tackled.
Stenning, D. C.; Wagner-Kaiser, R.; Robinson, E.; van Dyk, D. A.; von Hippel, T.; Sarajedini, A.; Stein, N.
2016-07-01
We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending earlier statistical models for open clusters composed of simple (single) stellar populations. Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties—age, metallicity, helium abundance, distance, absorption, and initial mass—are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two-population clusters, and also show how model misspecification can potentially be identified. As a proof of concept, we analyze the two stellar populations of globular cluster NGC 5272 using our model and methods. (BASE-9 is available from GitHub: https://github.com/argiopetech/base/releases).
Bayesian network reconstruction using systems genetics data: comparison of MCMC methods.
Tasaki, Shinya; Sauerwine, Ben; Hoff, Bruce; Toyoshiba, Hiroyoshi; Gaiteri, Chris; Chaibub Neto, Elias
2015-04-01
Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis-Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data.
In-medium dispersion relations of charmonia studied by the maximum entropy method
Ikeda, Atsuro; Asakawa, Masayuki; Kitazawa, Masakiyo
2017-01-01
We study in-medium spectral properties of charmonia in the vector and pseudoscalar channels at nonzero momenta on quenched lattices, especially focusing on their dispersion relation and the weight of the peak. We measure the lattice Euclidean correlation functions with nonzero momenta on the anisotropic quenched lattices and study the spectral functions with the maximum entropy method. The dispersion relations of charmonia and the momentum dependence of the weight of the peak are analyzed with the maximum entropy method together with the errors estimated probabilistically in this method. We find a significant increase of the masses of charmonia in medium. We also find that the functional form of the charmonium dispersion relations is not changed from that in the vacuum within the error even at T ≃1.6 Tc for all the channels we analyze.
DEFF Research Database (Denmark)
Madsen, Henrik; Rasmussen, Peter F.; Rosbjerg, Dan
1997-01-01
Two different models for analyzing extreme hydrologic events, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto distribution for modeling threshold exceedances corresponding to a generalized extreme value...... model with ML estimation for large positive shape parameters. Since heavy-tailed distributions, corresponding to negative shape parameters, are far the most common in hydrology, the PDS model generally is to be preferred for at-site quantile estimation....... distribution for annual maxima. The performance of the two models in terms of the uncertainty of the T-year event estimator is evaluated in the cases of estimation with, respectively, the maximum likelihood (ML) method, the method of moments (MOM), and the method of probability weighted moments (PWM...
Maximum-Entropy Meshfree Method for Compressible and Near-Incompressible Elasticity
Energy Technology Data Exchange (ETDEWEB)
Ortiz, A; Puso, M A; Sukumar, N
2009-09-04
Numerical integration errors and volumetric locking in the near-incompressible limit are two outstanding issues in Galerkin-based meshfree computations. In this paper, we present a modified Gaussian integration scheme on background cells for meshfree methods that alleviates errors in numerical integration and ensures patch test satisfaction to machine precision. Secondly, a locking-free small-strain elasticity formulation for meshfree methods is proposed, which draws on developments in assumed strain methods and nodal integration techniques. In this study, maximum-entropy basis functions are used; however, the generality of our approach permits the use of any meshfree approximation. Various benchmark problems in two-dimensional compressible and near-incompressible small strain elasticity are presented to demonstrate the accuracy and optimal convergence in the energy norm of the maximum-entropy meshfree formulation.
In-medium dispersion relations of charmonia studied by maximum entropy method
Ikeda, Atsuro; Kitazawa, Masakiyo
2016-01-01
We study in-medium spectral properties of charmonia in the vector and pseudoscalar channels at nonzero momenta on quenched lattices, especially focusing on their dispersion relation and weight of the peak. We measure the lattice Euclidean correlation functions with nonzero momenta on the anisotropic quenched lattices and study the spectral functions with the maximum entropy method. The dispersion relations of charmonia and the momentum dependence of the weight of the peak are analyzed with the maximum entropy method together with the errors estimated probabilistically in this method. We find significant increase of the masses of charmonia in medium. It is also found that the functional form of the charmonium dispersion relations is not changed from that in the vacuum within the error even at $T\\simeq1.6T_c$ for all the channels we analyzed.
A novel Bayesian DNA motif comparison method for clustering and retrieval.
Directory of Open Access Journals (Sweden)
Naomi Habib
2008-02-01
Full Text Available Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial: clustering of redundant motifs, and attributing the motifs to transcription factors by retrieval of similar motifs from previously characterized motif libraries. Both tasks inherently involve motif comparison. Here we present a novel method for comparing and merging motifs, based on Bayesian probabilistic principles. This method takes into account both the similarity in positional nucleotide distributions of the two motifs and their dissimilarity to the background distribution. We demonstrate the use of the new comparison method as a basis for motif clustering and retrieval procedures, and compare it to several commonly used alternatives. Our results show that the new method outperforms other available methods in accuracy and sensitivity. We incorporated the resulting motif clustering and retrieval procedures in a large-scale automated pipeline for analyzing DNA motifs. This pipeline integrates the results of various DNA motif discovery algorithms and automatically merges redundant motifs from multiple training sets into a coherent annotated library of motifs. Application of this pipeline to recent genome-wide transcription factor location data in S. cerevisiae successfully identified DNA motifs in a manner that is as good as semi-automated analysis reported in the literature. Moreover, we show how this analysis elucidates the mechanisms of condition-specific preferences of transcription factors.
Marasek, K; Nowicki, A
1994-01-01
The performance of three spectral techniques (FFT, AR Burg and ARMA) for maximum frequency estimation of the Doppler spectra is described. Different definitions of fmax were used: frequency at which spectral power decreases down to 0.1 of its maximum value, modified threshold crossing method (MTCM) and novel geometrical method. "Goodness" and efficiency of estimators were determined by calculating the bias and the standard deviation of the estimated maximum frequency of the simulated Doppler spectra with known statistics. The power of analysed signals was assumed to have the exponential distribution function. The SNR ratios were changed over the range from 0 to 20 dB. Different spectrum envelopes were generated. A Gaussian envelope approximated narrow band spectral processes (P. W. Doppler) and rectangular spectra were used to simulate a parabolic flow insonified with C. W. Doppler. The simulated signals were generated out of 3072-point records with sampling frequency of 20 kHz. The AR and ARMA models order selections were done independently according to Akaike Information Criterion (AIC) and Singular Value Decomposition (SVD). It was found that the ARMA model, computed according to SVD criterion, had the best overall performance and produced results with the smallest bias and standard deviation. In general AR(SVD) was better than AR(AIC). The geometrical method of fmax estimation was found to be more accurate than other tested methods, especially for narrow band signals.
WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.
Amgalan, Bayarbaatar; Lee, Hyunju
2014-01-01
Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC) method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function) over the standard simplex (sparsity constraints). We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.
A maximum-principle preserving finite element method for scalar conservation equations
Guermond, Jean-Luc
2014-04-01
This paper introduces a first-order viscosity method for the explicit approximation of scalar conservation equations with Lipschitz fluxes using continuous finite elements on arbitrary grids in any space dimension. Provided the lumped mass matrix is positive definite, the method is shown to satisfy the local maximum principle under a usual CFL condition. The method is independent of the cell type; for instance, the mesh can be a combination of tetrahedra, hexahedra, and prisms in three space dimensions. © 2014 Elsevier B.V.
Wheel-slip Control Method for Seeking Maximum Value of Tangential Force between Wheel and Rail
Kondo, Keiichiro; Yasuoka, Ikuo; Yamazaki, Osamu; Toda, Shinichi; Nakazawa, Yosuke
A method for reducing motor torque in proportion to wheel slip is applied to an inverter-driven electric locomotive. The motor torque at wheel-slip speed is less than the torque at the maximum tangential force or the adhesion force. A novel anti-slip control method for seeking the maximum value of the tangential force between the wheel and rail is proposed in this paper. The characteristics of the proposed method are analyzed theoretically to design the torque reduction ratio and the rate of change of the pattern between the wheel-slip speed and motor current. In addition, experimental tests are also carried out to verify that the use of the proposed method increases the traction force of an electric locomotive driven by induction motors and inverters. The experimental test results obtained by using the proposed control method are compared with the experimental results obtained by using a conventional control method. The averaged operational current when using the proposed control method is 10% more than that when using the conventional control method.
Mbakwe, Anthony C; Saka, Anthony A; Choi, Keechoo; Lee, Young-Jae
2016-08-01
Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes. This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research.
Uncertainty analysis of strain modal parameters by Bayesian method using frequency response function
Institute of Scientific and Technical Information of China (English)
Xu Li; Yi Weijian; Zhihua Yi
2007-01-01
Structural strain modes are able to detect changes in local structural performance, but errors are inevitably intermixed in the measured data. In this paper, strain modal parameters are considered as random variables, and their uncertainty is analyzed by a Bayesian method based on the structural frequency response function (FRF). The estimates of strain modal parameters with maximal posterior probability are determined. Several independent measurements of the FRF of a four-story reinforced concrete frame structural model were performed in the laboratory. The ability to identify the stiffness change in a concrete column using the strain mode was verified. It is shown that the uncertainty of the natural frequency is very small. Compared with the displacement mode shape, the variations of strain mode shapes at each point are quite different. The damping ratios are more affected by the types of test systems. Except for the case where a high order strain mode does not identify local damage, the first order strain mode can provide an exact indication of the damage location.
Real-time realizations of the Bayesian Infrasonic Source Localization Method
Pinsky, V.; Arrowsmith, S.; Hofstetter, A.; Nippress, A.
2015-12-01
The Bayesian Infrasonic Source Localization method (BISL), introduced by Mordak et al. (2010) and upgraded by Marcillo et al. (2014) is destined for the accurate estimation of the atmospheric event origin at local, regional and global scales by the seismic and infrasonic networks and arrays. The BISL is based on probabilistic models of the source-station infrasonic signal propagation time, picking time and azimuth estimate merged with a prior knowledge about celerity distribution. It requires at each hypothetical source location, integration of the product of the corresponding source-station likelihood functions multiplied by a prior probability density function of celerity over the multivariate parameter space. The present BISL realization is generally time-consuming procedure based on numerical integration. The computational scheme proposed simplifies the target function so that integrals are taken exactly and are represented via standard functions. This makes the procedure much faster and realizable in real-time without practical loss of accuracy. The procedure executed as PYTHON-FORTRAN code demonstrates high performance on a set of the model and real data.
Bayesian statistics an introduction
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
Zhao, W.; Cella, M.; Pasqua, O. Della; Burger, D.M.; Jacqz-Aigrain, E.
2012-01-01
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: Abacavir is used to treat HIV infection in both adults and children. The recommended paediatric dose is 8 mg kg(-1) twice daily up to a maximum of 300 mg twice daily. Weight was identified as the central covariate influencing pharmacokinetics of abacavir in
Energy Technology Data Exchange (ETDEWEB)
Barboza, Luciano Vitoria [Sul-riograndense Federal Institute for Education, Science and Technology (IFSul), Pelotas, RS (Brazil)], E-mail: luciano@pelotas.ifsul.edu.br
2009-07-01
This paper presents an overview about the maximum load ability problem and aims to study the main factors that limit this load ability. Specifically this study focuses its attention on determining which electric system buses influence directly on the power demand supply. The proposed approach uses the conventional maximum load ability method modelled by an optimization problem. The solution of this model is performed using the Interior Point methodology. As consequence of this solution method, the Lagrange multipliers are used as parameters that identify the probable 'bottlenecks' in the electric power system. The study also shows the relationship between the Lagrange multipliers and the cost function in the Interior Point optimization interpreted like sensitivity parameters. In order to illustrate the proposed methodology, the approach was applied to an IEEE test system and to assess its performance, a real equivalent electric system from the South- Southeast region of Brazil was simulated. (author)
Lattice Field Theory with the Sign Problem and the Maximum Entropy Method
Directory of Open Access Journals (Sweden)
Masahiro Imachi
2007-02-01
Full Text Available Although numerical simulation in lattice field theory is one of the most effective tools to study non-perturbative properties of field theories, it faces serious obstacles coming from the sign problem in some theories such as finite density QCD and lattice field theory with the θ term. We reconsider this problem from the point of view of the maximum entropy method.
1979-01-01
The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.
Maximum Energy Output of a DFIG Wind Turbine Using an Improved MPPT-Curve Method
Directory of Open Access Journals (Sweden)
Dinh-Chung Phan
2015-10-01
Full Text Available A new method is proposed for obtaining the maximum power output of a doubly-fed induction generator (DFIG wind turbine to control the rotor- and grid-side converters. The efficiency of maximum power point tracking that is obtained by the proposed method is theoretically guaranteed under assumptions that represent physical conditions. Several control parameters may be adjusted to ensure the quality of control performance. In particular, a DFIG state-space model and a control technique based on the Lyapunov function are adopted to derive the control method. The effectiveness of the proposed method is verified via numerical simulations of a 1.5-MW DFIG wind turbine using MATLAB/Simulink. The simulation results show that when the proposed method is used, the wind turbine is capable of properly tracking the optimal operation point; furthermore, the generator’s available energy output is higher when the proposed method is used than it is when the conventional method is used instead.
A Laplace method for under-determined Bayesian optimal experimental designs
Long, Quan
2014-12-17
In Long et al. (2013), a new method based on the Laplace approximation was developed to accelerate the estimation of the post-experimental expected information gains (Kullback–Leibler divergence) in model parameters and predictive quantities of interest in the Bayesian framework. A closed-form asymptotic approximation of the inner integral and the order of the corresponding dominant error term were obtained in the cases where the parameters are determined by the experiment. In this work, we extend that method to the general case where the model parameters cannot be determined completely by the data from the proposed experiments. We carry out the Laplace approximations in the directions orthogonal to the null space of the Jacobian matrix of the data model with respect to the parameters, so that the information gain can be reduced to an integration against the marginal density of the transformed parameters that are not determined by the experiments. Furthermore, the expected information gain can be approximated by an integration over the prior, where the integrand is a function of the posterior covariance matrix projected over the aforementioned orthogonal directions. To deal with the issue of dimensionality in a complex problem, we use either Monte Carlo sampling or sparse quadratures for the integration over the prior probability density function, depending on the regularity of the integrand function. We demonstrate the accuracy, efficiency and robustness of the proposed method via several nonlinear under-determined test cases. They include the designs of the scalar parameter in a one dimensional cubic polynomial function with two unidentifiable parameters forming a linear manifold, and the boundary source locations for impedance tomography in a square domain, where the unknown parameter is the conductivity, which is represented as a random field.
Jordan, Paul; Brunschwig, Hadassa; Luedin, Eric
2008-01-01
The approach of Bayesian mixed effects modeling is an appropriate method for estimating both population-specific as well as subject-specific times to steady state. In addition to pure estimation, the approach allows to determine the time until a certain fraction of individuals of a population has reached steady state with a pre-specified certainty. In this paper a mixed effects model for the parameters of a nonlinear pharmacokinetic model is used within a Bayesian framework. Model fitting by means of Markov Chain Monte Carlo methods as implemented in the Gibbs sampler as well as the extraction of estimates and probability statements of interest are described. Finally, the proposed approach is illustrated by application to trough data from a multiple dose clinical trial.
One-repetition maximum bench press performance estimated with a new accelerometer method.
Rontu, Jari-Pekka; Hannula, Manne I; Leskinen, Sami; Linnamo, Vesa; Salmi, Jukka A
2010-08-01
The one repetition maximum (1RM) is an important method to measure muscular strength. The purpose of this study was to evaluate a new method to predict 1RM bench press performance from a submaximal lift. The developed method was evaluated by using different load levels (50, 60, 70, 80, and 90% of 1RM). The subjects were active floorball players (n = 22). The new method is based on the assumption that the estimation of 1RM can be calculated from the submaximal weight and the maximum acceleration of the submaximal weight during the lift. The submaximal bench press lift was recorded with a 3-axis accelerometer integrated to a wrist equipment and a data acquisition card. The maximum acceleration was calculated from the measurement data of the sensor and analyzed in personal computer with LabView-based software. The estimated 1RM results were compared with traditionally measured 1RM results of the subjects. An own estimation equation was developed for each load level, that is, 5 different estimation equations have been used based on the measured 1RM values of the subjects. The mean (+/-SD) of measured 1RM result was 69.86 (+/-15.72) kg. The mean of estimated 1RM values were 69.85-69.97 kg. The correlations between measured and estimated 1RM results were high (0.89-0.97; p < 0.001). The differences between the methods were very small (-0.11 to 0.01 kg) and were not significantly different from each other. The results of this study showed promising prediction accuracy for estimating bench press performance by performing just a single submaximal bench press lift. The estimation accuracy is competitive with other known estimation methods, at least with the current study population.
Method for Building a Medical Training Simulator with Bayesian Networks: SimDeCS.
Flores, Cecilia Dias; Fonseca, João Marcelo; Bez, Marta Rosecler; Respício, Ana; Coelho, Helder
2014-01-01
Distance education has grown in importance with the advent of the internet. An adequate evaluation of students in this mode is still difficult. Distance tests or occasional on-site exams do not meet the needs of evaluation of the learning process for distance education. Bayesian networks are adequate for simulating several aspects of clinical reasoning. The possibility of integrating them in distance education student evaluation has not yet been explored much. The present work describes a Simulator based on probabilistic networks built to represent knowledge of clinical practice guidelines in Family and Community Medicine. The Bayesian Network, the basis of the simulator, was modeled to playable by the student, to give immediate feedback according to pedagogical strategies adapted to the student according to past performance, and to give a broad evaluation of performance at the end of the game. Simulators structured by Bayesian Networks may become alternatives in the evaluation of students of Medical Distance Education.
An improved maximum power point tracking method for a photovoltaic system
Ouoba, David; Fakkar, Abderrahim; El Kouari, Youssef; Dkhichi, Fayrouz; Oukarfi, Benyounes
2016-06-01
In this paper, an improved auto-scaling variable step-size Maximum Power Point Tracking (MPPT) method for photovoltaic (PV) system was proposed. To achieve simultaneously a fast dynamic response and stable steady-state power, a first improvement was made on the step-size scaling function of the duty cycle that controls the converter. An algorithm was secondly proposed to address wrong decision that may be made at an abrupt change of the irradiation. The proposed auto-scaling variable step-size approach was compared to some various other approaches from the literature such as: classical fixed step-size, variable step-size and a recent auto-scaling variable step-size maximum power point tracking approaches. The simulation results obtained by MATLAB/SIMULINK were given and discussed for validation.
A Load Balancing Algorithm Based on Maximum Entropy Methods in Homogeneous Clusters
Directory of Open Access Journals (Sweden)
Long Chen
2014-10-01
Full Text Available In order to solve the problems of ill-balanced task allocation, long response time, low throughput rate and poor performance when the cluster system is assigning tasks, we introduce the concept of entropy in thermodynamics into load balancing algorithms. This paper proposes a new load balancing algorithm for homogeneous clusters based on the Maximum Entropy Method (MEM. By calculating the entropy of the system and using the maximum entropy principle to ensure that each scheduling and migration is performed following the increasing tendency of the entropy, the system can achieve the load balancing status as soon as possible, shorten the task execution time and enable high performance. The result of simulation experiments show that this algorithm is more advanced when it comes to the time and extent of the load balance of the homogeneous cluster system compared with traditional algorithms. It also provides novel thoughts of solutions for the load balancing problem of the homogeneous cluster system.
Bayesian image reconstruction: Application to emission tomography
Energy Technology Data Exchange (ETDEWEB)
Nunez, J.; Llacer, J.
1989-02-01
In this paper we propose a Maximum a Posteriori (MAP) method of image reconstruction in the Bayesian framework for the Poisson noise case. We use entropy to define the prior probability and likelihood to define the conditional probability. The method uses sharpness parameters which can be theoretically computed or adjusted, allowing us to obtain MAP reconstructions without the problem of the grey'' reconstructions associated with the pre Bayesian reconstructions. We have developed several ways to solve the reconstruction problem and propose a new iterative algorithm which is stable, maintains positivity and converges to feasible images faster than the Maximum Likelihood Estimate method. We have successfully applied the new method to the case of Emission Tomography, both with simulated and real data. 41 refs., 4 figs., 1 tab.
Determination of zero-coupon and spot rates from treasury data by maximum entropy methods
Gzyl, Henryk; Mayoral, Silvia
2016-08-01
An interesting and important inverse problem in finance consists of the determination of spot rates or prices of the zero coupon bonds, when the only information available consists of the prices of a few coupon bonds. A variety of methods have been proposed to deal with this problem. Here we present variants of a non-parametric method to treat with such problems, which neither imposes an analytic form on the rates or bond prices, nor imposes a model for the (random) evolution of the yields. The procedure consists of transforming the problem of the determination of the prices of the zero coupon bonds into a linear inverse problem with convex constraints, and then applying the method of maximum entropy in the mean. This method is flexible enough to provide a possible solution to a mispricing problem.
Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel
2013-01-01
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean
DEFF Research Database (Denmark)
Vallejo, R L; Rexroad III, C E; Silverstein, J T
2009-01-01
As a first step toward the genetic mapping of QTL affecting stress response variation in rainbow trout, we performed complex segregation analyses (CSA) fitting mixed inheritance models of plasma cortisol by using Bayesian methods in large full-sib families of rainbow trout. To date, no studies have...... been conducted to determine the mode of inheritance of stress response as measured by plasma cortisol response when using a crowding stress paradigm and CSA in rainbow trout. The main objective of this study was to determine the mode of inheritance of plasma cortisol after a crowding stress....... The results from fitting mixed inheritance models with Bayesian CSA suggest that 1 or more major genes with dominant cortisol-decreasing alleles and small additive genetic effects of a large number of independent genes likely underlie the genetic variation of plasma cortisol in the rainbow trout families...
Shaweno, Debebe; Trauer, James M; Denholm, Justin T; McBryde, Emma S
2017-10-02
Reported tuberculosis (TB) incidence globally continues to be heavily influenced by expert opinion of case detection rates and ecological estimates of disease duration. Both approaches are recognised as having substantial variability and inaccuracy, leading to uncertainty in true TB incidence and other such derived statistics. We developed Bayesian binomial mixture geospatial models to estimate TB incidence and case detection rate (CDR) in Ethiopia. In these models the underlying true incidence was formulated as a partially observed Markovian process following a mixed Poisson distribution and the detected (observed) TB cases as a binomial distribution, conditional on CDR and true incidence. The models use notification data from multiple areas over several years and account for the existence of undetected TB cases and variability in true underlying incidence and CDR. Deviance information criteria (DIC) were used to select the best performing model. A geospatial model was the best fitting approach. This model estimated that TB incidence in Sheka Zone increased from 198 (95% Credible Interval (CrI) 187, 233) per 100,000 population in 2010 to 232 (95% CrI 212, 253) per 100,000 population in 2014. The model revealed a wide discrepancy between the estimated incidence rate and notification rate, with the estimated incidence ranging from 1.4 (in 2014) to 1.7 (in 2010) times the notification rate (CDR of 71% and 60% respectively). Population density and TB incidence in neighbouring locations (spatial lag) predicted the underlying TB incidence, while health facility availability predicted higher CDR. Our model estimated trends in underlying TB incidence while accounting for undetected cases and revealed significant discrepancies between incidence and notification rates in rural Ethiopia. This approach provides an alternative approach to estimating incidence, entirely independent of the methods involved in current estimates and is feasible to perform from routinely collected
Berglund, Håkan; O'Hara, Robert B; Jonsson, Bengt Gunnar
2009-10-01
Quantitative conservation objectives require detailed consideration of the habitat requirements of target species. Tree-living bryophytes, lichens, and fungi are a critical and declining biodiversity component of boreal forests. To understand their requirements, Bayesian methods were used to analyze the relationships between the occurrence of individual species and habitat factors at the tree and the stand scale in a naturally fragmented boreal forest landscape. The importance of unexplained between-stand variation in occurrence of species was estimated, and the ability of derived models to predict species' occurrence was tested. The occurrence of species was affected by quality of individual trees. Furthermore, the relationships between occurrence of species at the tree level and size and shape of stands indicated edge effects, implying that some species were restricted to interior habitats of large, regular stands. Yet for the habitat factors studied, requirements of many species appeared similar. Species occurrence also varied between stands; most of the seemingly suitable trees in some stands were unoccupied. The models captured most variation in species occurrence at tree level. They also successfully accounted for between-stand variation in species occurrence, thus providing realistic simulations of stand-level occupancy of species. Important unexplained between-stand variation in species occurrence warns against a simplified view that only local habitat factors influence species' occurrence. Apparently, similar stands will host populations of different sizes due to historical, spatial, and stochastic factors. Thus, habitat suitability cannot be assessed simply by population sizes, and stands lacking a species may still provide suitable habitat and merit protection.
Probabilistic Inferences in Bayesian Networks
Ding, Jianguo
2010-01-01
This chapter summarizes the popular inferences methods in Bayesian networks. The results demonstrates that the evidence can propagated across the Bayesian networks by any links, whatever it is forward or backward or intercausal style. The belief updating of Bayesian networks can be obtained by various available inference techniques. Theoretically, exact inferences in Bayesian networks is feasible and manageable. However, the computing and inference is NP-hard. That means, in applications, in ...
Festa, Roberto
1992-01-01
According to the Bayesian view, scientific hypotheses must be appraised in terms of their posterior probabilities relative to the available experimental data. Such posterior probabilities are derived from the prior probabilities of the hypotheses by applying Bayes'theorem. One of the most important
Institute of Scientific and Technical Information of China (English)
Sheng Zheng
2013-01-01
The estimation of lower atmospheric refractivity from radar sea clutter (RFC) is a complicated nonlinear optimization problem.This paper deals with the RFC problem in a Bayesian framework.It uses the unbiased Markov Chain Monte Carlo (MCMC) sampling technique,which can provide accurate posterior probability distributions of the estimated refractivity parameters by using an electromagnetic split-step fast Fourier transform terrain parabolic equation propagation model within a Bayesian inversion framework.In contrast to the global optimization algorithm,the Bayesian-MCMC can obtain not only the approximate solutions,but also the probability distributions of the solutions,that is,uncertainty analyses of solutions.The Bayesian-MCMC algorithm is implemented on the simulation radar sea-clutter data and the real radar seaclutter data.Reference data are assumed to be simulation data and refractivity profiles are obtained using a helicopter.The inversion algorithm is assessed (i) by comparing the estimated refractivity profiles from the assumed simulation and the helicopter sounding data; (ii) the one-dimensional (1D) and two-dimensional (2D) posterior probability distribution of solutions.
A method for real-time condition monitoring of haul roads based on Bayesian parameter estimation
CSIR Research Space (South Africa)
Heyns, T
2012-04-01
Full Text Available and to the vehicles. A recent idea is that vehicle on-board data collection systems could be used to monitor haul roads on a real-time basis by means of vibration signature analysis. This paper proposes a methodology based on Bayesian regression to isolate the effect...
Festa, Roberto
1992-01-01
According to the Bayesian view, scientific hypotheses must be appraised in terms of their posterior probabilities relative to the available experimental data. Such posterior probabilities are derived from the prior probabilities of the hypotheses by applying Bayes'theorem. One of the most important
A Bayesian Method for Evaluating Passing Scores: The PPoP Curve
Wainer, Howard; Wang, X. A.; Skorupski, William P.; Bradlow, Eric T.
2005-01-01
In this note, we demonstrate an interesting use of the posterior distributions (and corresponding posterior samples of proficiency) that are yielded by fitting a fully Bayesian test scoring model to a complex assessment. Specifically, we examine the efficacy of the test in combination with the specific passing score that was chosen through expert…
A novel Bayesian learning method for information aggregation in modular neural networks
DEFF Research Database (Denmark)
Wang, Pan; Xu, Lida; Zhou, Shang-Ming;
2010-01-01
Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight ...
A Hierarchical Bayesian M/EEG Imaging Method Correcting for Incomplete Spatio-Temporal Priors
DEFF Research Database (Denmark)
Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke;
2013-01-01
In this paper we present a hierarchical Bayesian model, to tackle the highly ill-posed problem that follows with MEG and EEG source imaging. Our model promotes spatiotemporal patterns through the use of both spatial and temporal basis functions. While in contrast to most previous spatio-temporal ...
A Bayesian analysis of rare B decays with advanced Monte Carlo methods
Energy Technology Data Exchange (ETDEWEB)
Beaujean, Frederik
2012-11-12
Searching for new physics in rare B meson decays governed by b {yields} s transitions, we perform a model-independent global fit of the short-distance couplings C{sub 7}, C{sub 9}, and C{sub 10} of the {Delta}B=1 effective field theory. We assume the standard-model set of b {yields} s{gamma} and b {yields} sl{sup +}l{sup -} operators with real-valued C{sub i}. A total of 59 measurements by the experiments BaBar, Belle, CDF, CLEO, and LHCb of observables in B{yields}K{sup *}{gamma}, B{yields}K{sup (*)}l{sup +}l{sup -}, and B{sub s}{yields}{mu}{sup +}{mu}{sup -} decays are used in the fit. Our analysis is the first of its kind to harness the full power of the Bayesian approach to probability theory. All main sources of theory uncertainty explicitly enter the fit in the form of nuisance parameters. We make optimal use of the experimental information to simultaneously constrain theWilson coefficients as well as hadronic form factors - the dominant theory uncertainty. Generating samples from the posterior probability distribution to compute marginal distributions and predict observables by uncertainty propagation is a formidable numerical challenge for two reasons. First, the posterior has multiple well separated maxima and degeneracies. Second, the computation of the theory predictions is very time consuming. A single posterior evaluation requires O(1s), and a few million evaluations are needed. Population Monte Carlo (PMC) provides a solution to both issues; a mixture density is iteratively adapted to the posterior, and samples are drawn in a massively parallel way using importance sampling. The major shortcoming of PMC is the need for cogent knowledge of the posterior at the initial stage. In an effort towards a general black-box Monte Carlo sampling algorithm, we present a new method to extract the necessary information in a reliable and automatic manner from Markov chains with the help of hierarchical clustering. Exploiting the latest 2012 measurements, the fit
Yatracos, Yannis G.
2013-01-01
The inherent bias pathology of the maximum likelihood (ML) estimation method is confirmed for models with unknown parameters $\\theta$ and $\\psi$ when MLE $\\hat \\psi$ is function of MLE $\\hat \\theta.$ To reduce $\\hat \\psi$'s bias the likelihood equation to be solved for $\\psi$ is updated using the model for the data $Y$ in it. Model updated (MU) MLE, $\\hat \\psi_{MU},$ often reduces either totally or partially $\\hat \\psi$'s bias when estimating shape parameter $\\psi.$ For the Pareto model $\\hat...
A Robust Image Tampering Detection Method Based on Maximum Entropy Criteria
Directory of Open Access Journals (Sweden)
Bo Zhao
2015-12-01
Full Text Available This paper proposes a novel image watermarking method based on local energy and maximum entropy aiming to improve the robustness. First, the image feature distribution is extracted by employing the local energy model and then it is transformed as a digital watermark by employing a Discrete Cosine Transform (DCT. An offset image is thus obtained according to the difference between the extracted digital watermarking and the feature distribution of the watermarked image. The entropy of the pixel value distribution is computed first. The Lorenz curve is used to measure the polarization degree of the pixel value distribution. In the pixel location distribution flow, the maximum entropy criteria is applied in segmenting the offset image into potentially tampered regions and unchanged regions. All-connected graph and 2-D Gaussian probability are utilized to obtain the probability distribution of the pixel location. Finally, the factitious tampering probability value of a pending detected image is computed through combining the weighting factors of pixel value and pixel location distribution. Experimental results show that the proposed method is more robust against the commonly used image processing operations, such as Gaussian noise, impulse noise, etc. Simultaneously, the proposed method achieves high sensitivity against factitious tampering.
Smolin, John A; Gambetta, Jay M; Smith, Graeme
2012-02-17
We provide an efficient method for computing the maximum-likelihood mixed quantum state (with density matrix ρ) given a set of measurement outcomes in a complete orthonormal operator basis subject to Gaussian noise. Our method works by first changing basis yielding a candidate density matrix μ which may have nonphysical (negative) eigenvalues, and then finding the nearest physical state under the 2-norm. Our algorithm takes at worst O(d(4)) for the basis change plus O(d(3)) for finding ρ where d is the dimension of the quantum state. In the special case where the measurement basis is strings of Pauli operators, the basis change takes only O(d(3)) as well. The workhorse of the algorithm is a new linear-time method for finding the closest probability distribution (in Euclidean distance) to a set of real numbers summing to one.
Matsumoto, Hisanori; Tokiwano, Kazuo; Hosoi, Hirotaka; Sueoka, Kazuhisa; Mukasa, Koichi
2002-05-01
We present a new technique for the restoration of scanning tunneling microscopy (STM) images, which is a two-dimensional extension of a recently developed statistical approach based on the one-dimensional least-squares method (LSM). An STM image is regarded as a realization of a stochastic process and assumed to be a composition of an underlying image and noise. We express the underlying image in terms of a two-dimensional generalized trigonometric polynomial suitable for representing the atomic protrusions in STM images. The optimization of the polynomial is performed by the two-dimensional LSM combined with the power spectral density function estimated by means of the maximum entropy method (MEM) iterative algorithm for two-dimensional signals. The restored images are obtained as the optimum least-squares fitting polynomial which is a continuous surface. We apply this technique to modeled and actual STM data. Results show that the present method yields a reasonable restoration of STM images.
Genetic Properties of Some Economic Traits in Isfahan Native Fowl Using Bayesian and REML Methods
Directory of Open Access Journals (Sweden)
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.
Filtering Additive Measurement Noise with Maximum Entropy in the Mean
Gzyl, Henryk
2007-01-01
The purpose of this note is to show how the method of maximum entropy in the mean (MEM) may be used to improve parametric estimation when the measurements are corrupted by large level of noise. The method is developed in the context on a concrete example: that of estimation of the parameter in an exponential distribution. We compare the performance of our method with the bayesian and maximum likelihood approaches.
Institute of Scientific and Technical Information of China (English)
XU Fu-min; XUE Hong-chao
2004-01-01
The Maximum Entropy Principle (MEP) method is elaborated, and the corresponding probability density evaluation method for the random fluctuation system is introduced, the goal of the article is to find the best fitting method for the wave climate statistical distribution. For the first time, a kind of new maximum entropy probability distribution (MEP distribution) expression is deduced in accordance with the second order moment of a random process. Different from all the fitting methods in the past, the MEP distribution can describe the probability distribution of any random fluctuation system conveniently and reasonably. If the moments of the random signal is limited to the second order, that is, the ratio of the root-mean-square value to the mean value of the random variable is obtained from the random sample, the corresponding MEP distribution can be computed according to the deduced expression in this essay. The concept of the wave climate is introduced here, and the MEP distribution is applied to fit the probability density distributions of the significant wave height and spectral peak period. Take the Mexico Gulf as an example, three stations at different locations, depths and wind wave strengths are chosen in the half-closed gulf, the significant wave height and spectral peak period distributions at each station are fitted with the MEP distribution, the Weibull distribution and the Log-normal distribution respectively, the fitted results are compared with the field observations, the results show that the MEP distribution is the best fitting method, and the Weibull distribution is the worst one when applied to the significant wave height and spectral peak period distributions at different locations, water depths and wind wave strengths in the Gulf. The conclusion shows the feasibility and reasonability of fitting wave climate statistical distributions with the deduced MEP distributions in this essay, and furthermore proves the great potential of MEP method to
What can we gain by using Bayesian methods to combine information from a multi-model ensemble?
Jonko, A. K.; Urban, N. M.
2016-12-01
Multi-model ensembles are used extensively to study both future climate projections and properties of the climate system such as climate sensitivity and feedbacks. Individual climate model projections generally disagree with one another, can be biased and are not independent. How to combine results from various models to assess their projections and the uncertainties associated with them is a difficult, but important question. Many different approaches, ranging from giving each model one vote, to model weighting and Bayesian methods, have been used to date. Here we evaluate the utility of a Bayesian reduced model framework relative to a simple pooling of global climate model (GCM) projections. Rather than focusing on the discrete projections made by individual GCMs, this approach allows us to generate probabilistic projections that smoothly interpolate between the dynamics of the multi-model ensemble. The simple model is an idealized ocean atmosphere energy balance model (EBM), fit to surface temperatures of GCMs participating in the Coupled Model Intercomparison Project version 5 (CMIP5) by tuning several parameters, including equilibrium climate sensitivity, forcing and feedback. We derive probability distributions of the reduced model parameters for each GCM individually as well as jointly for all GCMs in a Bayesian hierarchical modeling framework, using CMIP5 abrupt CO2 quadrupling simulations. We then compare climate sensitivity and feedback estimates as well as temperature projections for historical and RCP8.5 scenarios generated using these two approaches to results obtained from the multi-model ensemble alone.
Bajkova, Anisa T
2011-01-01
We propose the multi-frequency synthesis (MFS) algorithm with spectral correction of frequency-dependent source brightness distribution based on maximum entropy method. In order to take into account the spectral terms of n-th order in the Taylor expansion for the frequency-dependent brightness distribution, we use a generalized form of the maximum entropy method suitable for reconstruction of not only positive-definite functions, but also sign-variable ones. The proposed algorithm is aimed at producing both improved total intensity image and two-dimensional spectral index distribution over the source. We consider also the problem of frequency-dependent variation of the radio core positions of self-absorbed active galactic nuclei, which should be taken into account in a correct multi-frequency synthesis. First, the proposed MFS algorithm has been tested on simulated data and then applied to four-frequency synthesis imaging of the radio source 0954+658 from VLBA observational data obtained quasi-simultaneously ...
Peng, Hongtao; Lei, Tingwu; Jiang, Zhiyun; Horton, Robert
2016-06-01
Mulching of agricultural fields and gardens with pebbles has long been practiced to conserve soil moisture in some semi-arid regions with low precipitation. Rainfall interception by the pebble mulch itself is an important part of the computation of the water balance for the pebble mulched fields and gardens. The mean equivalent diameter (MED) was used to characterize the pebble size. The maximum static rainfall retention in pebble mulch is based on the water penetrating into the pores of pebbles, the water adhering to the outside surfaces of pebbles and the water held between pebbles of the mulch. Equations describing the water penetrating into the pores of pebbles and the water adhering to the outside surface of pebbles are constructed based on the physical properties of water and the pebble characteristics. The model for the water between pebbles of the mulch is based on the basic equation to calculate the water bridge volume and the basic coordination number model. A method to calculate the maximum static rainfall retention in the pebble mulch is presented. Laboratory rain simulation experiments were performed to test the model with measured data. Paired sample t-tests showed no significant differences between the values calculated with the method and the measured data. The model is ready for testing on field mulches.
Single Temperature Sensor Superheat Control Using a Novel Maximum Slope-seeking Method
DEFF Research Database (Denmark)
Vinther, Kasper; Rasmussen, Henrik; Izadi-Zamanabadi, Roozbeh;
2013-01-01
Superheating of refrigerant in the evaporator is an important aspect of safe operation of refrigeration systems. The level of superheat is typically controlled by adjusting the flow of refrigerant using an electronic expansion valve, where the superheat is calculated using measurements from...... a pressure and a temperature sensor. In this paper we show, through extensive testing, that the superheat or filling of the evaporator can actually be controlled using only a single temperature sensor. This can either reduce commissioning costs by lowering the necessary amount of sensors or add fault...... tolerance in existing systems if a sensor fails (e.g. pressure sensor). The solution is based on a novel maximum slope-seeking control method, where a perturbation signal is added to the valve opening degree, which gives additional information about the system for control purposes. Furthermore, the method...
Yin, Lo I.; Bielefeld, Michael J.
1987-01-01
The maximum entropy method (MEM) and balanced correlation method were used to reconstruct the images of low-intensity X-ray objects obtained experimentally by means of a uniformly redundant array coded aperture system. The reconstructed images from MEM are clearly superior. However, the MEM algorithm is computationally more time-consuming because of its iterative nature. On the other hand, both the inherently two-dimensional character of images and the iterative computations of MEM suggest the use of parallel processing machines. Accordingly, computations were carried out on the massively parallel processor at Goddard Space Flight Center as well as on the serial processing machine VAX 8600, and the results are compared.
An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions
Directory of Open Access Journals (Sweden)
Wei Wang
2014-01-01
Full Text Available We present an approximate nonsmooth algorithm to solve a minimization problem, in which the objective function is the sum of a maximum eigenvalue function of matrices and a convex function. The essential idea to solve the optimization problem in this paper is similar to the thought of proximal bundle method, but the difference is that we choose approximate subgradient and function value to construct approximate cutting-plane model to solve the above mentioned problem. An important advantage of the approximate cutting-plane model for objective function is that it is more stable than cutting-plane model. In addition, the approximate proximal bundle method algorithm can be given. Furthermore, the sequences generated by the algorithm converge to the optimal solution of the original problem.
Institute of Scientific and Technical Information of China (English)
周自强; 方守狮; 冯锋
2003-01-01
It is important to know the maximum solid solubility(Cmax) of various transition metals in a metal when one designs multi-component alloys. There have been several semi-empirical approaches to qualitatively predict the Cmax, such as Darken-Gurry(D-G) theorem, Miedema-Chelikowsky(M-C) theorem, electron concentration rule and the bond-parameter rule. However, they are not particularly valid for the prediction of Cmax. It was developed on the basis of energetics of alloys as a new method to predict Cmax of different transition metals in metal Ti, which can be described as a semi-empirical equation using the atomic parameters, i e, electronegativity difference, atomic diameter and electron concentration. It shows that the present method can be used to explain and deduce D-G theorem, M-C theorem and electron concentration rule.
Maximum-Likelihood Methods for Processing Signals From Gamma-Ray Detectors
Barrett, Harrison H.; Hunter, William C. J.; Miller, Brian William; Moore, Stephen K.; Chen, Yichun; Furenlid, Lars R.
2009-01-01
In any gamma-ray detector, each event produces electrical signals on one or more circuit elements. From these signals, we may wish to determine the presence of an interaction; whether multiple interactions occurred; the spatial coordinates in two or three dimensions of at least the primary interaction; or the total energy deposited in that interaction. We may also want to compute listmode probabilities for tomographic reconstruction. Maximum-likelihood methods provide a rigorous and in some senses optimal approach to extracting this information, and the associated Fisher information matrix provides a way of quantifying and optimizing the information conveyed by the detector. This paper will review the principles of likelihood methods as applied to gamma-ray detectors and illustrate their power with recent results from the Center for Gamma-ray Imaging. PMID:20107527
Maximum Reduced Proper Motion Method: Detection of New Nearby Ultracool Dwarfs
Phan-Bao, N
2011-01-01
In this paper, we describe how to use the Maximum Reduced Proper Motion method (Phan-Bao et al. 2003) to detect 57 nearby L and late-M dwarfs (d_phot <= 30 pc): 36 of them are newly discovered. Spectroscopic observations of 43 of the 57 ultracool dwarfs were previously reported in Martin et al. (2010). These ultracool dwarfs were identified by color criteria in ~5,000 square degrees of the DENIS database and then further selected by the method for spectroscopic follow-up to determine their spectral types and spectroscopic distances. We also report here our newly measured proper motions of these ultracool dwarfs from multi-epoch images found in public archives (ALADIN, DSS, 2MASS, DENIS), with at least three distinct epochs and time baselines of 2 to 46 years.
Statistical properties of the maximum Lyapunov exponent calculated via the divergence rate method.
Franchi, Matteo; Ricci, Leonardo
2014-12-01
The embedding of a time series provides a basic tool to analyze dynamical properties of the underlying chaotic system. To this purpose, the choice of the embedding dimension and lag is crucial. Although several methods have been devised to tackle the issue of the optimal setting of these parameters, a conclusive criterion to make the most appropriate choice is still lacking. An accepted procedure to rank different embedding methods relies on the evaluation of the maximum Lyapunov exponent (MLE) out of embedded time series that are generated by chaotic systems with explicit analytic representation. The MLE is evaluated as the local divergence rate of nearby trajectories. Given a system, embedding methods are ranked according to how close such MLE values are to the true MLE. This is provided by the so-called standard method in a way that exploits the mathematical description of the system and does not require embedding. In this paper we study the dependence of the finite-time MLE evaluated via the divergence rate method on the embedding dimension and lag in the case of time series generated by four systems that are widely used as references in the scientific literature. We develop a completely automatic algorithm that provides the divergence rate and its statistical uncertainty. We show that the uncertainty can provide useful information about the optimal choice of the embedding parameters. In addition, our approach allows us to find which systems provide suitable benchmarks for the comparison and ranking of different embedding methods.
Bayesian methods for estimating the reliability in complex hierarchical networks (interim report).
Energy Technology Data Exchange (ETDEWEB)
Marzouk, Youssef M.; Zurn, Rena M.; Boggs, Paul T.; Diegert, Kathleen V. (Sandia National Laboratories, Albuquerque, NM); Red-Horse, John Robert (Sandia National Laboratories, Albuquerque, NM); Pebay, Philippe Pierre
2007-05-01
Current work on the Integrated Stockpile Evaluation (ISE) project is evidence of Sandia's commitment to maintaining the integrity of the nuclear weapons stockpile. In this report, we undertake a key element in that process: development of an analytical framework for determining the reliability of the stockpile in a realistic environment of time-variance, inherent uncertainty, and sparse available information. This framework is probabilistic in nature and is founded on a novel combination of classical and computational Bayesian analysis, Bayesian networks, and polynomial chaos expansions. We note that, while the focus of the effort is stockpile-related, it is applicable to any reasonably-structured hierarchical system, including systems with feedback.
The Power of Principled Bayesian Methods in the Study of Stellar Evolution
von Hippel, Ted; Stenning, David C; Robinson, Elliot; Jeffery, Elizabeth; Stein, Nathan; Jefferys, William H; O'Malley, Erin
2016-01-01
It takes years of effort employing the best telescopes and instruments to obtain high-quality stellar photometry, astrometry, and spectroscopy. Stellar evolution models contain the experience of lifetimes of theoretical calculations and testing. Yet most astronomers fit these valuable models to these precious datasets by eye. We show that a principled Bayesian approach to fitting models to stellar data yields substantially more information over a range of stellar astrophysics. We highlight advances in determining the ages of star clusters, mass ratios of binary stars, limitations in the accuracy of stellar models, post-main-sequence mass loss, and the ages of individual white dwarfs. We also outline a number of unsolved problems that would benefit from principled Bayesian analyses.
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Raftery Adrian E
2009-02-01
Full Text Available Abstract Background Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes. Results We applied the iterative BMA algorithm to two cancer datasets: breast cancer and diffuse large B-cell lymphoma (DLBCL data. On the breast cancer data, the algorithm selected a total of 15 predictor genes across 84 contending models from the training data. The maximum likelihood estimates of the selected genes and the posterior probabilities of the selected models from the training data were used to divide patients in the test (or validation dataset into high- and low-risk categories. Using the genes and models determined from the training data, we assigned patients from the test data into highly distinct risk groups (as indicated by a p-value of 7.26e-05 from the log-rank test. Moreover, we achieved comparable results using only the 5 top selected genes with 100% posterior probabilities. On the DLBCL data, our iterative BMA procedure selected a total of 25 genes across 3 contending models from the training data. Once again, we assigned the patients in the validation set to significantly distinct risk groups (p
Directory of Open Access Journals (Sweden)
Sonali Sachin Sankpal
2016-01-01
Full Text Available Scattering and absorption of light is main reason for limited visibility in water. The suspended particles and dissolved chemical compounds in water are also responsible for scattering and absorption of light in water. The limited visibility in water results in degradation of underwater images. The visibility can be increased by using artificial light source in underwater imaging system. But the artificial light illuminates the scene in a nonuniform fashion. It produces bright spot at the center with the dark region at surroundings. In some cases imaging system itself creates dark region in the image by producing shadow on the objects. The problem of nonuniform illumination is neglected by the researchers in most of the image enhancement techniques of underwater images. Also very few methods are discussed showing the results on color images. This paper suggests a method for nonuniform illumination correction for underwater images. The method assumes that natural underwater images are Rayleigh distributed. This paper used maximum likelihood estimation of scale parameter to map distribution of image to Rayleigh distribution. The method is compared with traditional methods for nonuniform illumination correction using no-reference image quality metrics like average luminance, average information entropy, normalized neighborhood function, average contrast, and comprehensive assessment function.
Bayesian Network Assessment Method for Civil Aviation Safety Based on Flight Delays
2013-01-01
Flight delays and safety are the principal contradictions in the sound development of civil aviation. Flight delays often come up and induce civil aviation safety risk simultaneously. Based on flight delays, the random characteristics of civil aviation safety risk are analyzed. Flight delays have been deemed to a potential safety hazard. The change rules and characteristics of civil aviation safety risk based on flight delays have been analyzed. Bayesian networks (BN) have been used to build ...
Dembo, Mana; Radovčić, Davorka; Garvin, Heather M; Laird, Myra F; Schroeder, Lauren; Scott, Jill E; Brophy, Juliet; Ackermann, Rebecca R; Musiba, Chares M; de Ruiter, Darryl J; Mooers, Arne Ø; Collard, Mark
2016-08-01
Homo naledi is a recently discovered species of fossil hominin from South Africa. A considerable amount is already known about H. naledi but some important questions remain unanswered. Here we report a study that addressed two of them: "Where does H. naledi fit in the hominin evolutionary tree?" and "How old is it?" We used a large supermatrix of craniodental characters for both early and late hominin species and Bayesian phylogenetic techniques to carry out three analyses. First, we performed a dated Bayesian analysis to generate estimates of the evolutionary relationships of fossil hominins including H. naledi. Then we employed Bayes factor tests to compare the strength of support for hypotheses about the relationships of H. naledi suggested by the best-estimate trees. Lastly, we carried out a resampling analysis to assess the accuracy of the age estimate for H. naledi yielded by the dated Bayesian analysis. The analyses strongly supported the hypothesis that H. naledi forms a clade with the other Homo species and Australopithecus sediba. The analyses were more ambiguous regarding the position of H. naledi within the (Homo, Au. sediba) clade. A number of hypotheses were rejected, but several others were not. Based on the available craniodental data, Homo antecessor, Asian Homo erectus, Homo habilis, Homo floresiensis, Homo sapiens, and Au. sediba could all be the sister taxon of H. naledi. According to the dated Bayesian analysis, the most likely age for H. naledi is 912 ka. This age estimate was supported by the resampling analysis. Our findings have a number of implications. Most notably, they support the assignment of the new specimens to Homo, cast doubt on the claim that H. naledi is simply a variant of H. erectus, and suggest H. naledi is younger than has been previously proposed.
Nonlinear tracking in a diffusion process with a Bayesian filter and the finite element method
DEFF Research Database (Denmark)
Pedersen, Martin Wæver; Thygesen, Uffe Høgsbro; Madsen, Henrik
2011-01-01
A new approach to nonlinear state estimation and object tracking from indirect observations of a continuous time process is examined. Stochastic differential equations (SDEs) are employed to model the dynamics of the unobservable state. Tracking problems in the plane subject to boundaries...... become complicated using SMC because Monte Carlo randomness is introduced. The finite element (FE) method solves the Kolmogorov equations of the SDE numerically on a triangular unstructured mesh for which boundary conditions to the state-space are simple to incorporate. The FE approach to nonlinear state...... estimation is suited for off-line data analysis because the computed smoothed state densities, maximum a posteriori parameter estimates and state sequence are deterministic conditional on the finite element mesh and the observations. The proposed method is conceptually similar to existing point...
Colombani, C; Legarra, A; Fritz, S; Guillaume, F; Croiseau, P; Ducrocq, V; Robert-Granié, C
2013-01-01
Recently, the amount of available single nucleotide polymorphism (SNP) marker data has considerably increased in dairy cattle breeds, both for research purposes and for application in commercial breeding and selection programs. Bayesian methods are currently used in the genomic evaluation of dairy cattle to handle very large sets of explanatory variables with a limited number of observations. In this study, we applied 2 bayesian methods, BayesCπ and bayesian least absolute shrinkage and selection operator (LASSO), to 2 genotyped and phenotyped reference populations consisting of 3,940 Holstein bulls and 1,172 Montbéliarde bulls with approximately 40,000 polymorphic SNP. We compared the accuracy of the bayesian methods for the prediction of 3 traits (milk yield, fat content, and conception rate) with pedigree-based BLUP, genomic BLUP, partial least squares (PLS) regression, and sparse PLS regression, a variable selection PLS variant. The results showed that the correlations between observed and predicted phenotypes were similar in BayesCπ (including or not pedigree information) and bayesian LASSO for most of the traits and whatever the breed. In the Holstein breed, bayesian methods led to higher correlations than other approaches for fat content and were similar to genomic BLUP for milk yield and to genomic BLUP and PLS regression for the conception rate. In the Montbéliarde breed, no method dominated the others, except BayesCπ for fat content. The better performances of the bayesian methods for fat content in Holstein and Montbéliarde breeds are probably due to the effect of the DGAT1 gene. The SNP identified by the BayesCπ, bayesian LASSO, and sparse PLS regression methods, based on their effect on the different traits of interest, were located at almost the same position on the genome. As the bayesian methods resulted in regressions of direct genomic values on daughter trait deviations closer to 1 than for the other methods tested in this study, bayesian
Improvement of the detector resolution in X-ray spectrometry by using the maximum entropy method
Fernández, Jorge E.; Scot, Viviana; Giulio, Eugenio Di; Sabbatucci, Lorenzo
2015-11-01
In every X-ray spectroscopy measurement the influence of the detection system causes loss of information. Different mechanisms contribute to form the so-called detector response function (DRF): the detector efficiency, the escape of photons as a consequence of photoelectric or scattering interactions, the spectrum smearing due to the energy resolution, and, in solid states detectors (SSD), the charge collection artifacts. To recover the original spectrum, it is necessary to remove the detector influence by solving the so-called inverse problem. The maximum entropy unfolding technique solves this problem by imposing a set of constraints, taking advantage of the known a priori information and preserving the positive-defined character of the X-ray spectrum. This method has been included in the tool UMESTRAT (Unfolding Maximum Entropy STRATegy), which adopts a semi-automatic strategy to solve the unfolding problem based on a suitable combination of the codes MAXED and GRAVEL, developed at PTB. In the past UMESTRAT proved the capability to resolve characteristic peaks which were revealed as overlapped by a Si SSD, giving good qualitative results. In order to obtain quantitative results, UMESTRAT has been modified to include the additional constraint of the total number of photons of the spectrum, which can be easily determined by inverting the diagonal efficiency matrix. The features of the improved code are illustrated with some examples of unfolding from three commonly used SSD like Si, Ge, and CdTe. The quantitative unfolding can be considered as a software improvement of the detector resolution.
Bayesian ensemble refinement by replica simulations and reweighting.
Hummer, Gerhard; Köfinger, Jürgen
2015-12-28
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.
Energy Technology Data Exchange (ETDEWEB)
Karagiannis, Georgios; Lin, Guang
2014-02-15
Generalized polynomial chaos (gPC) expansions allow the representation of the solution of a stochastic system as a series of polynomial terms. The number of gPC terms increases dramatically with the dimension of the random input variables. When the number of the gPC terms is larger than that of the available samples, a scenario that often occurs if the evaluations of the system are expensive, the evaluation of the gPC expansion can be inaccurate due to over-fitting. We propose a fully Bayesian approach that allows for global recovery of the stochastic solution, both in spacial and random domains, by coupling Bayesian model uncertainty and regularization regression methods. It allows the evaluation of the PC coefficients on a grid of spacial points via (1) Bayesian model average or (2) medial probability model, and their construction as functions on the spacial domain via spline interpolation. The former accounts the model uncertainty and provides Bayes-optimal predictions; while the latter, additionally, provides a sparse representation of the solution by evaluating the expansion on a subset of dominating gPC bases when represented as a gPC expansion. Moreover, the method quantifies the importance of the gPC bases through inclusion probabilities. We design an MCMC sampler that evaluates all the unknown quantities without the need of ad-hoc techniques. The proposed method is suitable for, but not restricted to, problems whose stochastic solution is sparse at the stochastic level with respect to the gPC bases while the deterministic solver involved is expensive. We demonstrate the good performance of the proposed method and make comparisons with others on 1D, 14D and 40D in random space elliptic stochastic partial differential equations.
Meillier, Céline; Chatelain, Florent; Michel, Olivier; Bacon, Roland; Piqueras, Laure; Bacher, Raphael; Ayasso, Hacheme
2016-04-01
We present SELFI, the Source Emission Line FInder, a new Bayesian method optimized for detection of faint galaxies in Multi Unit Spectroscopic Explorer (MUSE) deep fields. MUSE is the new panoramic integral field spectrograph at the Very Large Telescope (VLT) that has unique capabilities for spectroscopic investigation of the deep sky. It has provided data cubes with 324 million voxels over a single 1 arcmin2 field of view. To address the challenge of faint-galaxy detection in these large data cubes, we developed a new method that processes 3D data either for modeling or for estimation and extraction of source configurations. This object-based approach yields a natural sparse representation of the sources in massive data fields, such as MUSE data cubes. In the Bayesian framework, the parameters that describe the observed sources are considered random variables. The Bayesian model leads to a general and robust algorithm where the parameters are estimated in a fully data-driven way. This detection algorithm was applied to the MUSE observation of Hubble Deep Field-South. With 27 h total integration time, these observations provide a catalog of 189 sources of various categories and with secured redshift. The algorithm retrieved 91% of the galaxies with only 9% false detection. This method also allowed the discovery of three new Lyα emitters and one [OII] emitter, all without any Hubble Space Telescope counterpart. We analyzed the reasons for failure for some targets, and found that the most important limitation of the method is when faint sources are located in the vicinity of bright spatially resolved galaxies that cannot be approximated by the Sérsic elliptical profile. The software and its documentation are available on the MUSE science web service (muse-vlt.eu/science).
Energy Technology Data Exchange (ETDEWEB)
Carletta, Nicholas D.; Mullendore, Gretchen L.; Starzec, Mariusz; Xi, Baike; Feng, Zhe; Dong, Xiquan
2016-08-01
Convective mass transport is the transport of mass from near the surface up to the upper troposphere and lower stratosphere (UTLS) by a deep convective updraft. This transport can alter the chemical makeup and water vapor balance of the UTLS, which affects cloud formation and the radiative properties of the atmosphere. It is therefore important to understand the exact altitudes at which mass is detrained from convection. The purpose of this study was to improve upon previously published methodologies for estimating the level of maximum detrainment (LMD) within convection using data from a single ground-based radar. Four methods were used to identify the LMD and validated against dual-Doppler derived vertical mass divergence fields for six cases with a variety of storm types. The best method for locating the LMD was determined to be the method that used a reflectivity texture technique to determine convective cores and a multi-layer echo identification to determine anvil locations. Although an improvement over previously published methods, the new methodology still produced unreliable results in certain regimes. The methodology worked best when applied to mature updrafts, as the anvil needs time to grow to a detectable size. Thus, radar reflectivity is found to be valuable in estimating the LMD, but storm maturity must also be considered for best results.
Woldegebriel, Michael
2015-11-17
In toxicology screening (forensic, food-safety), due to several analytical errors (e.g., retention time shift, lack of repeatability in m/z scans, etc.), the ability to confidently identify/confirm a compound remains a challenge. Due to these uncertainties, a probabilistic approach is currently preferred. However, if a probabilistic approach is followed, the only statistical method that is capable of estimating the probability of whether the compound of interest (COI) is present/absent in a given sample is Bayesian statistics. Bayes' theorem can combine prior information (prior probability) with data (likelihood) to give an optimal probability (posterior probability) reflecting the presence/absence of the COI. In this work, a novel method for calculating an informative prior probability for a Bayesian model in targeted toxicology screening is introduced. In contrast to earlier proposals making use of literature citation rates and the prior knowledge of the analyst, this method presents a thorough and nonsubjective approach. The formulation approaches the probability calculation as a clustering and random draw problem that incorporates few analytical method parameters meticulously estimated to reflect sensitivity and specificity of the system. The practicality of the method has been demonstrated and validated using real data and simulated analytical techniques.
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
In order to restrain the mid-spatial frequency error in magnetorheological finishing (MRF) process, a novel part-random path is designed based on the theory of maximum entropy method (MEM). Using KDMRF-1000F polishing machine, one flat work piece (98 mm in diameter) is polished. The mid-spatial frequency error in the region using part-random path is much lower than that by using common raster path. After one MRF iteration (7.46 min), peak-to-valley (PV) is 0.062 wave (1 wave =632.8 nm), root-mean-square (RMS) is 0.010 wave and no obvious mid-spatial frequency error is found. The result shows that the part-random path is a novel path, which results in a high form accuracy and low mid-spatial frequency error in MRF process.
Improved incremental conductance method for maximum power point tracking using cuk converter
Directory of Open Access Journals (Sweden)
M. Saad Saoud
2014-03-01
Full Text Available The Algerian government relies on a strategy focused on the development of inexhaustible resources such as solar and uses to diversify energy sources and prepare the Algeria of tomorrow: about 40% of the production of electricity for domestic consumption will be from renewable sources by 2030, Therefore it is necessary to concentrate our forces in order to reduce the application costs and to increment their performances, Their performance is evaluated and compared through theoretical analysis and digital simulation. This paper presents simulation of improved incremental conductance method for maximum power point tracking (MPPT using DC-DC cuk converter. This improved algorithm is used to track MPPs because it performs precise control under rapidly changing Atmospheric conditions, Matlab/ Simulink were employed for simulation studies.
Jafarizadeh, M A; Sabric, H; Malekic, B Rashidian
2011-01-01
In this paper,a systematic study of quantum phase transition within U(5) \\leftrightarrow SO(6) limits is presented in terms of infinite dimensional Algebraic technique in the IBM framework. Energy level statistics are investigated with Maximum Likelihood Estimation (MLE) method in order to characterize transitional region. Eigenvalues of these systems are obtained by solving Bethe-Ansatz equations with least square fitting processes to experimental data to obtain constants of Hamiltonian. Our obtained results verify the dependence of Nearest Neighbor Spacing Distribution's (NNSD) parameter to control parameter (c_{s}) and also display chaotic behavior of transitional regions in comparing with both limits. In order to compare our results for two limits with both GUE and GOE ensembles, we have suggested a new NNSD distribution and have obtained better KLD distances for the new distribution in compared with others in both limits. Also in the case of N\\to\\infty, the total boson number dependence displays the univ...
Directory of Open Access Journals (Sweden)
Xintao Xia
2013-07-01
Full Text Available This study proposed the bootstrap maximum-entropy method to evaluate the uncertainty of the starting torque of a slewing bearing. Addressing the variation coefficient of the slewing bearing starting torque under load, the probability density function, estimated true value and variation domain are obtained through experimental investigation of the slewing bearing starting torque under various loads. The probability density function is found to be characterized by variational figure, scale and location. In addition, the estimated true value and the variation domain vary from large to small along with increasing load, indicating better evolution of the stability and reliability of the starting friction torque. Finally, a sensitive spot exists where the estimated true value and the variation domain rise abnormally, showing a fluctuation in the immunity and a degenerative disorder in the stability and reliability of the starting friction torque.
High resolution VLBI polarisation imaging of AGN with the Maximum Entropy Method
Coughlan, Colm P
2016-01-01
Radio polarisation images of the jets of Active Galactic Nuclei (AGN) can provide a deep insight into the launching and collimation mechanisms of relativistic jets. However, even at VLBI scales, resolution is often a limiting factor in the conclusions that can be drawn from observations. The Maximum Entropy Method (MEM) is a deconvolution algorithm that can outperform the more common CLEAN algorithm in many cases, particularly when investigating structures present on scales comparable to or smaller than the nominal beam size with "super-resolution". A new implementation of the MEM suitable for single- or multiple-wavelength VLBI polarisation observations has been developed and is described here. Monte Carlo simulations comparing the performances of CLEAN and MEM at reconstructing the properties of model images are presented; these demonstrate the enhanced reliability of MEM over CLEAN when images of the fractional polarisation and polarisation angle are constructed using convolving beams that are appreciably ...
Imaging VLBI polarimetry data from Active Galactic Nuclei using the Maximum Entropy Method
Directory of Open Access Journals (Sweden)
Coughlan Colm P.
2013-12-01
Full Text Available Mapping the relativistic jets emanating from AGN requires the use of a deconvolution algorithm to account for the effects of missing baseline spacings. The CLEAN algorithm is the most commonly used algorithm in VLBI imaging today and is suitable for imaging polarisation data. The Maximum Entropy Method (MEM is presented as an alternative with some advantages over the CLEAN algorithm, including better spatial resolution and a more rigorous and unbiased approach to deconvolution. We have developed a MEM code suitable for deconvolving VLBI polarisation data. Monte Carlo simulations investigating the performance of CLEAN and the MEM code on a variety of source types are being carried out. Real polarisation (VLBA data taken at multiple wavelengths have also been deconvolved using MEM, and several of the resulting polarisation and Faraday rotation maps are presented and discussed.
Proposed method to construct Boolean functions with maximum possible annihilator immunity
Goyal, Rajni; Panigrahi, Anupama; Bansal, Rohit
2017-07-01
Nonlinearity and Algebraic(annihilator) immunity are two core properties of a Boolean function because optimum values of Annihilator Immunity and nonlinearity are required to resist fast algebraic attack and differential cryptanalysis respectively. For a secure cypher system, Boolean function(S-Boxes) should resist maximum number of attacks. It is possible if a Boolean function has optimal trade-off among its properties. Before constructing Boolean functions, we fixed the criteria of our constructions based on its properties. In present work, our construction is based on annihilator immunity and nonlinearity. While keeping above facts in mind,, we have developed a multi-objective evolutionary approach based on NSGA-II and got the optimum value of annihilator immunity with good bound of nonlinearity. We have constructed balanced Boolean functions having the best trade-off among balancedness, Annihilator immunity and nonlinearity for 5, 6 and 7 variables by the proposed method.
Fox, G.J.A.; Berg, van den S.M.; Veldkamp, B.P.; Irwing, P.; Booth, T.; Hughes, D.
2015-01-01
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item resp
Fox, Gerardus J.A.; van den Berg, Stéphanie Martine; Veldkamp, Bernard P.; Irwing, P.; Booth, T.; Hughes, D.
2015-01-01
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item
Granade, Christopher; Cory, D G
2015-01-01
In recent years, Bayesian methods have been proposed as a solution to a wide range of issues in quantum state and process tomography. State-of- the-art Bayesian tomography solutions suffer from three problems: numerical intractability, a lack of informative prior distributions, and an inability to track time-dependent processes. Here, we solve all three problems. First, we use modern statistical methods, as pioneered by Husz\\'ar and Houlsby and by Ferrie, to make Bayesian tomography numerically tractable. Our approach allows for practical computation of Bayesian point and region estimators for quantum states and channels. Second, we propose the first informative priors on quantum states and channels. Finally, we develop a method that allows online tracking of time-dependent states and estimates the drift and diffusion processes affecting a state. We provide source code and animated visual examples for our methods.
Miao, Yonghao; Zhao, Ming; Lin, Jing; Lei, Yaguo
2017-08-01
The extraction of periodic impulses, which are the important indicators of rolling bearing faults, from vibration signals is considerably significance for fault diagnosis. Maximum correlated kurtosis deconvolution (MCKD) developed from minimum entropy deconvolution (MED) has been proven as an efficient tool for enhancing the periodic impulses in the diagnosis of rolling element bearings and gearboxes. However, challenges still exist when MCKD is applied to the bearings operating under harsh working conditions. The difficulties mainly come from the rigorous requires for the multi-input parameters and the complicated resampling process. To overcome these limitations, an improved MCKD (IMCKD) is presented in this paper. The new method estimates the iterative period by calculating the autocorrelation of the envelope signal rather than relies on the provided prior period. Moreover, the iterative period will gradually approach to the true fault period through updating the iterative period after every iterative step. Since IMCKD is unaffected by the impulse signals with the high kurtosis value, the new method selects the maximum kurtosis filtered signal as the final choice from all candidates in the assigned iterative counts. Compared with MCKD, IMCKD has three advantages. First, without considering prior period and the choice of the order of shift, IMCKD is more efficient and has higher robustness. Second, the resampling process is not necessary for IMCKD, which is greatly convenient for the subsequent frequency spectrum analysis and envelope spectrum analysis without resetting the sampling rate. Third, IMCKD has a significant performance advantage in diagnosing the bearing compound-fault which expands the application range. Finally, the effectiveness and superiority of IMCKD are validated by a number of simulated bearing fault signals and applying to compound faults and single fault diagnosis of a locomotive bearing.
Voils, Corrine; Hassselblad, Vic; Crandell, Jamie; Chang, Yunkyung; Lee, Eunjeong; Sandelowski, Margarete
2009-10-01
Bayesian meta-analysis is a frequently cited but very little-used method for synthesizing qualitative and quantitative research findings. The only example published to date used qualitative data to generate an informative prior probability and quantitative data to generate the likelihood. We developed a method to incorporate both qualitative and quantitative evidence in the likelihood in a Bayesian synthesis of evidence about the relationship between regimen complexity and medication adherence. Data were from 11 qualitative and six quantitative studies. We updated two different non-informative prior distributions with qualitative and quantitative findings to find the posterior distribution for the probabilities that a more complex regimen was associated with lower adherence and that a less complex regimen was associated with greater adherence. The posterior mode for the qualitative findings regarding more complex regimen and lesser adherence (using the uniform prior with Jeffreys' prior yielding highly similar estimates) was 0.588 (95% credible set limits 0.519, 0.663) and for the quantitative findings was 0.224 (0.203, 0.245); due to non-overlapping credible sets, we did not combine them. The posterior mode for the qualitative findings regarding less complex regimen and greater adherence was 0.288 (0.214, 0.441) and for the quantitative findings was 0.272 (0.118, 0.437); the combined estimate was 0.299 (0.267, 0.334). The utility of Bayesian methods for synthesizing qualitative and quantitative research findings at the participant level may depend on the nature of the relationship being synthesized and on how well the findings are represented in the individual reports.
Osborne, S F
1984-02-01
The medical issues that arise in the isolated environment of a submarine can occasionally be grave. While crewmembers are carefully screened for health problems, they are still susceptible to serious acute illness. Currently, the submarine medical department representative, the hospital corpsman, utilizes a history and physical examination, clinical acumen, and limited laboratory testing in diagnosis. The application of a Bayesian method of analysis to an abdominal pain diagnostic system utilizing an onboard microcomputer is described herein. Early results from sea trials show an appropriate diagnosis in eight of 10 cases of abdominal pain, but the program should still be viewed as an extended "laboratory test" until proved effective at sea.
Ghajarnia, Navid; Arasteh, Peyman D.; Araghinejad, Shahab; Liaghat, Majid A.
2016-07-01
Incorrect estimation of rainfall occurrence, so called False Alarm (FA) is one of the major sources of bias error of satellite based precipitation estimation products and may even cause lots of problems during the bias reduction and calibration processes. In this paper, a hybrid statistical method is introduced to detect FA events of PERSIANN dataset over Urmia Lake basin in northwest of Iran. The main FA detection model is based on Bayesian theorem at which four predictor parameters including PERSIANN rainfall estimations, brightness temperature (Tb), precipitable water (PW) and near surface air temperature (Tair) is considered as its input dataset. In order to decrease the dimensions of input dataset by summarizing their most important modes of variability and correlations to the reference dataset, a technique named singular value decomposition (SVD) is used. The application of Bayesian-SVD method in FA detection of Urmia Lake basin resulted in a trade-off between FA detection and Hit events loss. The results show success of proposed method in detecting about 30% of FA events in return for loss of about 12% of Hit events while better capability of this method in cold seasons is observed.
Regional analysis of annual maximum rainfall using TL-moments method
Shabri, Ani Bin; Daud, Zalina Mohd; Ariff, Noratiqah Mohd
2011-06-01
Information related to distributions of rainfall amounts are of great importance for designs of water-related structures. One of the concerns of hydrologists and engineers is the probability distribution for modeling of regional data. In this study, a novel approach to regional frequency analysis using L-moments is revisited. Subsequently, an alternative regional frequency analysis using the TL-moments method is employed. The results from both methods were then compared. The analysis was based on daily annual maximum rainfall data from 40 stations in Selangor Malaysia. TL-moments for the generalized extreme value (GEV) and generalized logistic (GLO) distributions were derived and used to develop the regional frequency analysis procedure. TL-moment ratio diagram and Z-test were employed in determining the best-fit distribution. Comparison between the two approaches showed that the L-moments and TL-moments produced equivalent results. GLO and GEV distributions were identified as the most suitable distributions for representing the statistical properties of extreme rainfall in Selangor. Monte Carlo simulation was used for performance evaluation, and it showed that the method of TL-moments was more efficient for lower quantile estimation compared with the L-moments.
Evolutionary analysis of apolipoprotein E by Maximum Likelihood and complex network methods
Directory of Open Access Journals (Sweden)
Leandro de Jesus Benevides
Full Text Available Abstract Apolipoprotein E (apo E is a human glycoprotein with 299 amino acids, and it is a major component of very low density lipoproteins (VLDL and a group of high-density lipoproteins (HDL. Phylogenetic studies are important to clarify how various apo E proteins are related in groups of organisms and whether they evolved from a common ancestor. Here, we aimed at performing a phylogenetic study on apo E carrying organisms. We employed a classical and robust method, such as Maximum Likelihood (ML, and compared the results using a more recent approach based on complex networks. Thirty-two apo E amino acid sequences were downloaded from NCBI. A clear separation could be observed among three major groups: mammals, fish and amphibians. The results obtained from ML method, as well as from the constructed networks showed two different groups: one with mammals only (C1 and another with fish (C2, and a single node with the single sequence available for an amphibian. The accordance in results from the different methods shows that the complex networks approach is effective in phylogenetic studies. Furthermore, our results revealed the conservation of apo E among animal groups.
Energy Technology Data Exchange (ETDEWEB)
Zhang, Guannan [ORNL; Webster, Clayton G [ORNL; Gunzburger, Max D [ORNL
2012-09-01
Although Bayesian analysis has become vital to the quantification of prediction uncertainty in groundwater modeling, its application has been hindered due to the computational cost associated with numerous model executions needed for exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, we develop a new approach that improves computational efficiency of Bayesian inference by constructing a surrogate system based on an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, we utilize a compactly supported higher-order hierar- chical basis to construct the surrogate system, resulting in a significant reduction in the number of computational simulations required. In addition, we use hierarchical surplus as an error indi- cator to determine adaptive sparse grids. This allows local refinement in the uncertain domain and/or anisotropic detection with respect to the random model parameters, which further improves computational efficiency. Finally, we incorporate a global optimization technique and propose an iterative algorithm for building the surrogate system for the PPDF with multiple significant modes. Once the surrogate system is determined, the PPDF can be evaluated by sampling the surrogate system directly with very little computational cost. The developed method is evaluated first using a simple analytical density function with multiple modes and then using two synthetic groundwater reactive transport models. The groundwater models represent different levels of complexity; the first example involves coupled linear reactions and the second example simulates nonlinear ura- nium surface complexation. The results show that the aSG-hSC is an effective and efficient tool for Bayesian inference in groundwater modeling in comparison with conventional
Bayman, Emine O; Chaloner, Kathryn M; Hindman, Bradley J; Todd, Michael M
2013-01-16
To quantify the variability among centers and to identify centers whose performance are potentially outside of normal variability in the primary outcome and to propose a guideline that they are outliers. Novel statistical methodology using a Bayesian hierarchical model is used. Bayesian methods for estimation and outlier detection are applied assuming an additive random center effect on the log odds of response: centers are similar but different (exchangeable). The Intraoperative Hypothermia for Aneurysm Surgery Trial (IHAST) is used as an example. Analyses were adjusted for treatment, age, gender, aneurysm location, World Federation of Neurological Surgeons scale, Fisher score and baseline NIH stroke scale scores. Adjustments for differences in center characteristics were also examined. Graphical and numerical summaries of the between-center standard deviation (sd) and variability, as well as the identification of potential outliers are implemented. In the IHAST, the center-to-center variation in the log odds of favorable outcome at each center is consistent with a normal distribution with posterior sd of 0.538 (95% credible interval: 0.397 to 0.726) after adjusting for the effects of important covariates. Outcome differences among centers show no outlying centers. Four potential outlying centers were identified but did not meet the proposed guideline for declaring them as outlying. Center characteristics (number of subjects enrolled from the center, geographical location, learning over time, nitrous oxide, and temporary clipping use) did not predict outcome, but subject and disease characteristics did. Bayesian hierarchical methods allow for determination of whether outcomes from a specific center differ from others and whether specific clinical practices predict outcome, even when some centers/subgroups have relatively small sample sizes. In the IHAST no outlying centers were found. The estimated variability between centers was moderately large.
How about a Bayesian M/EEG imaging method correcting for incomplete spatio-temporal priors
DEFF Research Database (Denmark)
Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke;
2013-01-01
In this contribution we present a hierarchical Bayesian model, sAquavit, to tackle the highly ill-posed problem that follows with MEG and EEG source imaging. Our model facilitates spatio-temporal patterns through the use of both spatial and temporal basis functions. While in contrast to most...... previous spatio-temporal inverse M/EEG models, the proposed model benefits of consisting of two source terms, namely, a spatio-temporal pattern term limiting the source configuration to a spatio-temporal subspace and a source correcting term to pick up source activity not covered by the spatio...
Dose-Response Modeling Under Simple Order Restrictions Using Bayesian Variable Selection Methods
Otava, Martin; Shkedy, Ziv; Lin, Dan; Goehlmann, Hinrich W. H.; Bijnens, Luc; Talloen, Willem; Kasim, Adetayo
2014-01-01
Bayesian modeling of dose–response data offers the possibility to establish the relationship between a clinical or a genomic response and increasing doses of a therapeutic compound and to determine the nature of the relationship wherever it exists. In this article, we focus on an order-restricted one-way ANOVA model which can be used to test the null hypothesis of no dose effect against an ordered alternative. Within the framework of the dose–response modeling, a model uncertainty can be addr...
Analysis of simulated fluorescence intensities decays by a new maximum entropy method algorithm.
Esposito, Rosario; Altucci, Carlo; Velotta, Raffaele
2013-01-01
A new algorithm for the Maximum Entropy Method (MEM) is proposed for recovering the lifetime distribution in time-resolved fluorescence decays. The procedure is based on seeking the distribution that maximizes the Skilling entropy function subjected to the chi-squared constraint χ(2) ~ 1 through iterative linear approximations, LU decomposition of the Hessian matrix of the lagrangian problem and the Golden Section Search for backtracking. The accuracy of this algorithm has been investigated through comparisons with simulated fluorescence decays both of narrow and broad lifetime distributions. The proposed approach is capable to analyse datasets of up to 4,096 points with a discretization ranging from 100 to 1,000 lifetimes. A good agreement with non linear fitting estimates has been observed when the method has been applied to multi-exponential decays. Remarkable results have been also obtained for the broad lifetime distributions where the position is recovered with high accuracy and the distribution width is estimated within 3%. These results indicate that the procedure proposed generates MEM lifetime distributions that can be used to quantify the real heterogeneity of lifetimes in a sample.
Maximum ikelihood estimation for the double-count method with independent observers
Manly, Bryan F.J.; McDonald, Lyman L.; Garner, Gerald W.
1996-01-01
Data collected under a double-count protocol during line transect surveys were analyzed using new maximum likelihood methods combined with Akaike's information criterion to provide estimates of the abundance of polar bear (Ursus maritimus Phipps) in a pilot study off the coast of Alaska. Visibility biases were corrected by modeling the detection probabilities using logistic regression functions. Independent variables that influenced the detection probabilities included perpendicular distance of bear groups from the flight line and the number of individuals in the groups. A series of models were considered which vary from (1) the simplest, where the probability of detection was the same for both observers and was not affected by either distance from the flight line or group size, to (2) models where probability of detection is different for the two observers and depends on both distance from the transect and group size. Estimation procedures are developed for the case when additional variables may affect detection probabilities. The methods are illustrated using data from the pilot polar bear survey and some recommendations are given for design of a survey over the larger Chukchi Sea between Russia and the United States.
Sheen, D. H.; Seong, Y. J.; Park, J. H.; Lim, I. S.
2015-12-01
From the early of this year, the Korea Meteorological Administration (KMA) began to operate the first stage of an earthquake early warning system (EEWS) and provide early warning information to the general public. The earthquake early warning system (EEWS) in the KMA is based on the Earthquake Alarm Systems version 2 (ElarmS-2), developed at the University of California Berkeley. This method estimates the earthquake location using a simple grid search algorithm that finds the location with the minimum variance of the origin time on successively finer grids. A robust maximum likelihood earthquake location (MAXEL) method for early warning, based on the equal differential times of P arrivals, was recently developed. The MAXEL has been demonstrated to be successful in determining the event location, even when an outlier is included in the small number of P arrivals. This presentation details the application of the MAXEL to the EEWS of the KMA, its performance evaluation over seismic networks in South Korea with synthetic data, and comparison of statistics of earthquake locations based on the ElarmS-2 and the MAXEL.
Current trends in Bayesian methodology with applications
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
de Beer, Alex G F; Samson, Jean-Sebastièn; Hua, Wei; Huang, Zishuai; Chen, Xiangke; Allen, Heather C; Roke, Sylvie
2011-12-14
We present a direct comparison of phase sensitive sum-frequency generation experiments with phase reconstruction obtained by the maximum entropy method. We show that both methods lead to the same complex spectrum. Furthermore, we discuss the strengths and weaknesses of each of these methods, analyzing possible sources of experimental and analytical errors. A simulation program for maximum entropy phase reconstruction is available at: http://lbp.epfl.ch/.
Zhang, Dengfeng; Nakaya, Naoshi; Koui, Yuuji; Yoshida, Hitoaki
Recently, the appearance frequency of computer virus variants has increased. Updates to virus information using the normal pattern matching method are increasingly unable to keep up with the speed at which viruses occur, since it takes time to extract the characteristic patterns for each virus. Therefore, a rapid, automatic virus detection algorithm using static code analysis is necessary. However, recent computer viruses are almost always compressed and obfuscated. It is difficult to determine the characteristics of the binary code from the obfuscated computer viruses. Therefore, this paper proposes a method that unpacks compressed computer viruses automatically independent of the compression format. The proposed method unpacks the common compression formats accurately 80% of the time, while unknown compression formats can also be unpacked. The proposed method is effective against unknown viruses by combining it with the existing known virus detection system like Paul Graham's Bayesian Virus Filter etc.
THE GENERALIZED MAXIMUM LIKELIHOOD METHOD APPLIED TO HIGH PRESSURE PHASE EQUILIBRIUM
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Lúcio CARDOZO-FILHO
1997-12-01
Full Text Available The generalized maximum likelihood method was used to determine binary interaction parameters between carbon dioxide and components of orange essential oil. Vapor-liquid equilibrium was modeled with Peng-Robinson and Soave-Redlich-Kwong equations, using a methodology proposed in 1979 by Asselineau, Bogdanic and Vidal. Experimental vapor-liquid equilibrium data on binary mixtures formed with carbon dioxide and compounds usually found in orange essential oil were used to test the model. These systems were chosen to demonstrate that the maximum likelihood method produces binary interaction parameters for cubic equations of state capable of satisfactorily describing phase equilibrium, even for a binary such as ethanol/CO2. Results corroborate that the Peng-Robinson, as well as the Soave-Redlich-Kwong, equation can be used to describe phase equilibrium for the following systems: components of essential oil of orange/CO2.Foi empregado o método da máxima verossimilhança generalizado para determinação de parâmetros de interação binária entre os componentes do óleo essencial de laranja e dióxido de carbono. Foram usados dados experimentais de equilíbrio líquido-vapor de misturas binárias de dióxido de carbono e componentes do óleo essencial de laranja. O equilíbrio líquido-vapor foi modelado com as equações de Peng-Robinson e de Soave-Redlich-Kwong usando a metodologia proposta em 1979 por Asselineau, Bogdanic e Vidal. A escolha destes sistemas teve como objetivo demonstrar que o método da máxima verosimilhança produz parâmetros de interação binária, para equações cúbicas de estado capazes de descrever satisfatoriamente até mesmo o equilíbrio para o binário etanol/CO2. Os resultados comprovam que tanto a equação de Peng-Robinson quanto a de Soave-Redlich-Kwong podem ser empregadas para descrever o equilíbrio de fases para o sistemas: componentes do óleo essencial de laranja/CO2.
Bayesian methods for the design and interpretation of clinical trials in very rare diseases
Hampson, Lisa V; Whitehead, John; Eleftheriou, Despina; Brogan, Paul
2014-01-01
This paper considers the design and interpretation of clinical trials comparing treatments for conditions so rare that worldwide recruitment efforts are likely to yield total sample sizes of 50 or fewer, even when patients are recruited over several years. For such studies, the sample size needed to meet a conventional frequentist power requirement is clearly infeasible. Rather, the expectation of any such trial has to be limited to the generation of an improved understanding of treatment options. We propose a Bayesian approach for the conduct of rare-disease trials comparing an experimental treatment with a control where patient responses are classified as a success or failure. A systematic elicitation from clinicians of their beliefs concerning treatment efficacy is used to establish Bayesian priors for unknown model parameters. The process of determining the prior is described, including the possibility of formally considering results from related trials. As sample sizes are small, it is possible to compute all possible posterior distributions of the two success rates. A number of allocation ratios between the two treatment groups can be considered with a view to maximising the prior probability that the trial concludes recommending the new treatment when in fact it is non-inferior to control. Consideration of the extent to which opinion can be changed, even by data from the best feasible design, can help to determine whether such a trial is worthwhile. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24957522
Bayesian methods for the design and interpretation of clinical trials in very rare diseases.
Hampson, Lisa V; Whitehead, John; Eleftheriou, Despina; Brogan, Paul
2014-10-30
This paper considers the design and interpretation of clinical trials comparing treatments for conditions so rare that worldwide recruitment efforts are likely to yield total sample sizes of 50 or fewer, even when patients are recruited over several years. For such studies, the sample size needed to meet a conventional frequentist power requirement is clearly infeasible. Rather, the expectation of any such trial has to be limited to the generation of an improved understanding of treatment options. We propose a Bayesian approach for the conduct of rare-disease trials comparing an experimental treatment with a control where patient responses are classified as a success or failure. A systematic elicitation from clinicians of their beliefs concerning treatment efficacy is used to establish Bayesian priors for unknown model parameters. The process of determining the prior is described, including the possibility of formally considering results from related trials. As sample sizes are small, it is possible to compute all possible posterior distributions of the two success rates. A number of allocation ratios between the two treatment groups can be considered with a view to maximising the prior probability that the trial concludes recommending the new treatment when in fact it is non-inferior to control. Consideration of the extent to which opinion can be changed, even by data from the best feasible design, can help to determine whether such a trial is worthwhile.
A viable method for goodness-of-fit test in maximum likelihood fit
Institute of Scientific and Technical Information of China (English)
张锋; 高原宁; 霍雷
2011-01-01
A test statistic is proposed to perform the goodness-of-fit test in the unbinned maximum likelihood fit. Without using a detailed expression of the efficiency function, the test statistic is found to be strongly correlated with the maximum likelihood func
Takeda, Osamu; Iwamoto, Hirone; Sakashita, Ryota; Iseki, Chiaki; Zhu, Hongmin
2017-07-01
A surface tension measurement method based on the maximum bubble pressure (MBP) method was developed in order to precisely determine the surface tension of molten silicates in this study. Specifically, the influence of viscosity on surface tension measurements was quantified, and the criteria for accurate measurement were investigated. It was found that the MBP apparently increased with an increase in viscosity. This was because extra pressure was required for the flowing liquid inside the capillary due to viscous resistance. It was also expected that the extra pressure would decrease by decreasing the fluid velocity. For silicone oil with a viscosity of 1000 \\hbox {mPa}{\\cdot }\\hbox {s}, the error on the MBP could be decreased to +1.7 % by increasing the bubble detachment time to 300 \\hbox {s}. However, the error was still over 1 % even when the bubble detachment time was increased to 600 \\hbox {s}. Therefore, a true value of the MBP was determined by using a curve-fitting technique with a simple relaxation function, and that was succeeded for silicone oil at 1000 \\hbox {mPa}{\\cdot } \\hbox {s} of viscosity. Furthermore, for silicone oil with a viscosity as high as 10 000 \\hbox {mPa}{\\cdot }\\hbox {s}, the apparent MBP approached a true value by interrupting the gas introduction during the pressure rising period and by re-introducing the gas at a slow flow rate. Based on the fundamental investigation at room temperature, the surface tension of the \\hbox {SiO}2-40 \\hbox {mol}%\\hbox {Na}2\\hbox {O} and \\hbox {SiO}2-50 \\hbox {mol}%\\hbox {Na}2\\hbox {O} melts was determined at a high temperature. The obtained value was slightly lower than the literature values, which might be due to the influence of viscosity on surface tension measurements being removed in this study.
Rizzo, D. M.; Fytilis, N.; Stevens, L.
2012-12-01
Environmental managers are increasingly required to monitor and forecast long-term effects and vulnerability of biophysical systems to human-generated stresses. Ideally, a study involving both physical and biological assessments conducted concurrently (in space and time) could provide a better understanding of the mechanisms and complex relationships. However, costs and resources associated with monitoring the complex linkages between the physical, geomorphic and habitat conditions and the biological integrity of stream reaches are prohibitive. Researchers have used classification techniques to place individual streams and rivers into a broader spatial context (hydrologic or health condition). Such efforts require environmental managers to gather multiple forms of information - quantitative, qualitative and subjective. We research and develop a novel classification tool that combines self-organizing maps with a Naïve Bayesian classifier to direct resources to stream reaches most in need. The Vermont Agency of Natural Resources has developed and adopted protocols for physical stream geomorphic and habitat assessments throughout the state of Vermont. Separate from these assessments, the Vermont Department of Environmental Conservation monitors the biological communities and the water quality in streams. Our initial hypothesis is that the geomorphic reach assessments and water quality data may be leveraged to reduce error and uncertainty associated with predictions of biological integrity and stream health. We test our hypothesis using over 2500 Vermont stream reaches (~1371 stream miles) assessed by the two agencies. In the development of this work, we combine a Naïve Bayesian classifier with a modified Kohonen Self-Organizing Map (SOM). The SOM is an unsupervised artificial neural network that autonomously analyzes inherent dataset properties using input data only. It is typically used to cluster data into similar categories when a priori classes do not exist. The
Kedzierska, Anna; Husmeier, Dirk
2006-01-01
We propose a heuristic approach to the detection of evidence for recombination and gene conversion in multiple DNA sequence alignments. The proposed method consists of two stages. In the first stage, a sliding window is moved along the DNA sequence alignment, and phylogenetic trees are sampled from the conditional posterior distribution with MCMC. To reduce the noise intrinsic to inference from the limited amount of data available in the typically short sliding window, a clustering algorithm based on the Robinson-Foulds distance is applied to the trees thus sampled, and the posterior distribution over tree clusters is obtained for each window position. While changes in this posterior distribution are indicative of recombination or gene conversion events, it is difficult to decide when such a change is statistically significant. This problem is addressed in the second stage of the proposed algorithm, where the distributions obtained in the first stage are post-processed with a Bayesian hidden Markov model (HMM). The emission states of the HMM are associated with posterior distributions over phylogenetic tree topology clusters. The hidden states of the HMM indicate putative recombinant segments. Inference is done in a Bayesian sense, sampling parameters from the posterior distribution with MCMC. Of particular interest is the determination of the number of hidden states as an indication of the number of putative recombinant regions. To this end, we apply reversible jump MCMC, and sample the number of hidden states from the respective posterior distribution.
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Corsaro Enrico
2015-01-01
Full Text Available The peak bagging analysis, namely the fitting and identification of single oscillation modes in stars’ power spectra, coupled to the very high-quality light curves of red giant stars observed by Kepler, can play a crucial role for studying stellar oscillations of different flavor with an unprecedented level of detail. A thorough study of stellar oscillations would thus allow for deeper testing of stellar structure models and new insights in stellar evolution theory. However, peak bagging inferences are in general very challenging problems due to the large number of observed oscillation modes, hence of free parameters that can be involved in the fitting models. Efficiency and robustness in performing the analysis is what may be needed to proceed further. For this purpose, we developed a new code implementing the Nested Sampling Monte Carlo (NSMC algorithm, a powerful statistical method well suited for Bayesian analyses of complex problems. In this talk we show the peak bagging of a sample of high signal-to-noise red giant stars by exploiting recent Kepler datasets and a new criterion for the detection of an oscillation mode based on the computation of the Bayesian evidence. Preliminary results for frequencies and lifetimes for single oscillation modes, together with acoustic glitches, are therefore presented.
Bayesian Decision-theoretic Methods for Parameter Ensembles with Application to Epidemiology
Ginestet, Cedric E
2011-01-01
Parameter ensembles or sets of random effects constitute one of the cornerstones of modern statistical practice. This is especially the case in Bayesian hierarchical models, where several decision theoretic frameworks can be deployed. The estimation of these parameter ensembles may substantially vary depending on which inferential goals are prioritised by the modeller. Since one may wish to satisfy a range of desiderata, it is therefore of interest to investigate whether some sets of point estimates can simultaneously meet several inferential objectives. In this thesis, we will be especially concerned with identifying ensembles of point estimates that produce good approximations of (i) the true empirical quantiles and empirical quartile ratio (QR) and (ii) provide an accurate classification of the ensemble's elements above and below a given threshold. For this purpose, we review various decision-theoretic frameworks, which have been proposed in the literature in relation to the optimisation of different aspec...
Bayesian methods for the physical sciences learning from examples in astronomy and physics
Andreon, Stefano
2015-01-01
Statistical literacy is critical for the modern researcher in Physics and Astronomy. This book empowers researchers in these disciplines by providing the tools they will need to analyze their own data. Chapters in this book provide a statistical base from which to approach new problems, including numerical advice and a profusion of examples. The examples are engaging analyses of real-world problems taken from modern astronomical research. The examples are intended to be starting points for readers as they learn to approach their own data and research questions. Acknowledging that scientific progress now hinges on the availability of data and the possibility to improve previous analyses, data and code are distributed throughout the book. The JAGS symbolic language used throughout the book makes it easy to perform Bayesian analysis and is particularly valuable as readers may use it in a myriad of scenarios through slight modifications.
A Bayesian method for inferring transmission chains in a partially observed epidemic.
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Marzouk, Youssef M.; Ray, Jaideep
2008-10-01
We present a Bayesian approach for estimating transmission chains and rates in the Abakaliki smallpox epidemic of 1967. The epidemic affected 30 individuals in a community of 74; only the dates of appearance of symptoms were recorded. Our model assumes stochastic transmission of the infections over a social network. Distinct binomial random graphs model intra- and inter-compound social connections, while disease transmission over each link is treated as a Poisson process. Link probabilities and rate parameters are objects of inference. Dates of infection and recovery comprise the remaining unknowns. Distributions for smallpox incubation and recovery periods are obtained from historical data. Using Markov chain Monte Carlo, we explore the joint posterior distribution of the scalar parameters and provide an expected connectivity pattern for the social graph and infection pathway.
2011-01-01
Background Since the discovery of the "living fossil" in 1938, the coelacanth (Latimeria chalumnae) has generally been considered to be the closest living relative of the land vertebrates, and this is still the prevailing opinion in most general biology textbooks. However, the origin of tetrapods has not been resolved for decades. Three principal hypotheses (lungfish-tetrapod, coelacanth-tetrapod, or lungfish-coelacanth sister group) have been proposed. Findings We used the Bayesian method under the coalescence model with the latest published program (Bayesian Estimation of Species Trees, or BEST) to perform a phylogenetic analysis for seven relevant taxa and 43 nuclear protein-coding genes with the jackknife method for taxon sub-sampling. The lungfish-coelacanth sister group was consistently reconstructed with the Bayesian method under the coalescence model in 17 out of 21 taxon sets with a Bayesian posterior probability as high as 99%. Lungfish-tetrapod was only inferred from BCLS and BACLS. Neither coelacanth-tetrapod nor lungfish-coelacanth-tetrapod was recovered out of all 21 taxon sets. Conclusions Our results provide strong evidence in favor of accepting the hypothesis that lungfishes and coelacanths form a monophyletic sister-group that is the closest living relative of tetrapods. This clade was supported by high Bayesian posterior probabilities of the branch (a lungfish-coelacanth clade) and high taxon jackknife supports. PMID:21385375
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Gras Robin
2011-03-01
Full Text Available Abstract Background Since the discovery of the "living fossil" in 1938, the coelacanth (Latimeria chalumnae has generally been considered to be the closest living relative of the land vertebrates, and this is still the prevailing opinion in most general biology textbooks. However, the origin of tetrapods has not been resolved for decades. Three principal hypotheses (lungfish-tetrapod, coelacanth-tetrapod, or lungfish-coelacanth sister group have been proposed. Findings We used the Bayesian method under the coalescence model with the latest published program (Bayesian Estimation of Species Trees, or BEST to perform a phylogenetic analysis for seven relevant taxa and 43 nuclear protein-coding genes with the jackknife method for taxon sub-sampling. The lungfish-coelacanth sister group was consistently reconstructed with the Bayesian method under the coalescence model in 17 out of 21 taxon sets with a Bayesian posterior probability as high as 99%. Lungfish-tetrapod was only inferred from BCLS and BACLS. Neither coelacanth-tetrapod nor lungfish-coelacanth-tetrapod was recovered out of all 21 taxon sets. Conclusions Our results provide strong evidence in favor of accepting the hypothesis that lungfishes and coelacanths form a monophyletic sister-group that is the closest living relative of tetrapods. This clade was supported by high Bayesian posterior probabilities of the branch (a lungfish-coelacanth clade and high taxon jackknife supports.
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Gabriella Ferruzzi
2013-02-01
Full Text Available A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence of the solar radiation on some meteorological variables, such as the cloud cover and humidity. Then, a Monte Carlo simulation procedure is used to evaluate the predictive probability density function of the hourly active power by applying the photovoltaic system model to the random sampling of the clearness index distribution. A numerical application demonstrates the effectiveness and advantages of the proposed forecasting method.
High resolution VLBI polarization imaging of AGN with the maximum entropy method
Coughlan, Colm P.; Gabuzda, Denise C.
2016-12-01
Radio polarization images of the jets of Active Galactic Nuclei (AGN) can provide a deep insight into the launching and collimation mechanisms of relativistic jets. However, even at VLBI scales, resolution is often a limiting factor in the conclusions that can be drawn from observations. The maximum entropy method (MEM) is a deconvolution algorithm that can outperform the more common CLEAN algorithm in many cases, particularly when investigating structures present on scales comparable to or smaller than the nominal beam size with `super-resolution'. A new implementation of the MEM suitable for single- or multiple-wavelength VLBI polarization observations has been developed and is described here. Monte Carlo simulations comparing the performances of CLEAN and MEM at reconstructing the properties of model images are presented; these demonstrate the enhanced reliability of MEM over CLEAN when images of the fractional polarization and polarization angle are constructed using convolving beams that are appreciably smaller than the full CLEAN beam. The results of using this new MEM software to image VLBA observations of the AGN 0716+714 at six different wavelengths are presented, and compared to corresponding maps obtained with CLEAN. MEM and CLEAN maps of Stokes I, the polarized flux, the fractional polarization and the polarization angle are compared for convolving beams ranging from the full CLEAN beam down to a beam one-third of this size. MEM's ability to provide more trustworthy polarization imaging than a standard CLEAN-based deconvolution when convolving beams appreciably smaller than the full CLEAN beam are used is discussed.
Institute of Scientific and Technical Information of China (English)
张贝; 李卫东; 杨勇; 汪善勤; 蔡崇法
2011-01-01
The Bayesian maximum entropy ( BME) approach has emerged in recent years as a new spatio-temporal geostatistics methods. By capitalizing on various sources of information and data, BME introduces an epistemological framework which produces predictive maps that are more accurate and in many cases computationally more efficient than those derived with traditional techniques. It is a general approach that does not need to make assumptions regarding linear valuation, spatial homogeneity or normal distribution. BME can integrate a priori knowledge and soft data without losing any useful information they contain and improve accuracy of the analysis. This paper first introduces the basic theory of BME and stages of BME estimation, and then briefly describes its development and application in soil and environmental sciences. Finally the application of this method is also summarized and prospected. After years of development and practice, the BME method has been proved to be a mature outstanding approach, which has a broad prospect of application in evaluation of resources and environment.%贝叶斯最大熵(Bayesian Maximum Entropy,BME)地统计学方法是近年来出现的一种时空地 统计学新方法.相对于传统的克里金方法,该法具有坚实的认识论框架和方法学基础.它不需要作线性估 值、空间匀质和正态分布的假设,能够融入先验知识和软数据,并且不会损失其中蕴含的有用信息,提高了分 析精度.本文首先介绍了BME的基本理论及其估值方法,随后简单描述了该方法的理论发展过程及其在土 壤和环境科学上的应用情况,最后对该方法的应用做了总结与展望.经过国外研究者多年的开发和实践, BME方法已经被证明是一个理论上较为成熟,能够应用到实际研究中的优秀地统计学方法,在资源环境评估 上有着广泛的应用前景.
A Bayesian approach to estimating causal vaccine effects on binary post-infection outcomes.
Zhou, Jincheng; Chu, Haitao; Hudgens, Michael G; Halloran, M Elizabeth
2016-01-15
To estimate causal effects of vaccine on post-infection outcomes, Hudgens and Halloran (2006) defined a post-infection causal vaccine efficacy estimand VEI based on the principal stratification framework. They also derived closed forms for the maximum likelihood estimators of the causal estimand under some assumptions. Extending their research, we propose a Bayesian approach to estimating the causal vaccine effects on binary post-infection outcomes. The identifiability of the causal vaccine effect VEI is discussed under different assumptions on selection bias. The performance of the proposed Bayesian method is compared with the maximum likelihood method through simulation studies and two case studies - a clinical trial of a rotavirus vaccine candidate and a field study of pertussis vaccination. For both case studies, the Bayesian approach provided similar inference as the frequentist analysis. However, simulation studies with small sample sizes suggest that the Bayesian approach provides smaller bias and shorter confidence interval length.
Bayesian Face Sketch Synthesis.
Wang, Nannan; Gao, Xinbo; Sun, Leiyu; Li, Jie
2017-03-01
Exemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical models. The proposed Bayesian framework consists of two parts: the neighbor selection model and the weight computation model. Within the proposed framework, we further propose a Bayesian face sketch synthesis method. The essential rationale behind the proposed Bayesian method is that we take the spatial neighboring constraint between adjacent image patches into consideration for both aforementioned models, while the state-of-the-art methods neglect the constraint either in the neighbor selection model or in the weight computation model. Extensive experiments on the Chinese University of Hong Kong face sketch database demonstrate that the proposed Bayesian method could achieve superior performance compared with the state-of-the-art methods in terms of both subjective perceptions and objective evaluations.
Takeda, K.; Ochiai, H.; Takeuchi, S.
1985-01-01
Maximum snow water equivalence and snowcover distribution are estimated using several LANDSAT data taken in snowmelting season over a four year period. The test site is Okutadami-gawa Basin located in the central position of Tohoku-Kanto-Chubu District. The year to year normalization for snowmelt volume computation on the snow line is conducted by year to year correction of degree days using the snowcover percentage within the test basin obtained from LANDSAT data. The maximum snow water equivalent map in the test basin is generated based on the normalized snowmelt volume on the snow line extracted from four LANDSAT data taken in a different year. The snowcover distribution on an arbitrary day in snowmelting of 1982 is estimated from the maximum snow water equivalent map. The estimated snowcover is compared with the snowcover area extracted from NOAA-AVHRR data taken on the same day. The applicability of the snow estimation using LANDSAT data is discussed.
新家, 健精
2013-01-01
© 2012 Springer Science+Business Media, LLC. All rights reserved. Article Outline: Glossary Definition of the Subject and Introduction The Bayesian Statistical Paradigm Three Examples Comparison with the Frequentist Statistical Paradigm Future Directions Bibliography
Classification using Bayesian neural nets
J.C. Bioch (Cor); O. van der Meer; R. Potharst (Rob)
1995-01-01
textabstractRecently, Bayesian methods have been proposed for neural networks to solve regression and classification problems. These methods claim to overcome some difficulties encountered in the standard approach such as overfitting. However, an implementation of the full Bayesian approach to neura
Wu, J -L; Xiao, H
2015-01-01
Model-form uncertainties in complex mechanics systems are a major obstacle for predictive simulations. Reducing these uncertainties is critical for stake-holders to make risk-informed decisions based on numerical simulations. For example, Reynolds-Averaged Navier-Stokes (RANS) simulations are increasingly used in mission-critical systems involving turbulent flows. However, for many practical flows the RANS predictions have large model-form uncertainties originating from the uncertainty in the modeled Reynolds stresses. Recently, a physics-informed Bayesian framework has been proposed to quantify and reduce model-form uncertainties in RANS simulations by utilizing sparse observation data. However, in the design stage of engineering systems, measurement data are usually not available. In the present work we extend the original framework to scenarios where there are no available data on the flow to be predicted. In the proposed method, we first calibrate the model discrepancy on a related flow with available dat...
Maximum Power Point Tracking Method For PV Array Under Partially Shaded Condition
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Belqasem Aljafari
2016-08-01
Full Text Available Solar radiation that hits the photovoltaic modules has a variable character depending on the position, the direction of the solar field, the season, and the hour of the day. During the trajectory of a day, a shadow may be decanted on the cell, which may be contemplated, as in the case of a building near the solar field, or unforeseeable as those created by clouds. The breakthrough of PV systems as distributed power generation systems has increased drastically in the last few years. Because of this Maximum Power Point Tracking (MPPT is becoming more and more substantial as the amount of energy generated by PV systems is increasing. A MPPT technique must be used to track the maximum power point since the MPP depends on solar irradiation and cell temperature. In general, when the impedances of the load and source are matched, the maximum power is transferred to the load from the source only. The generated energy from PV systems must be maximized, as the efficiency of solar panels is low. For that reason to get the maximum power, a PV system is repeatedly equipped with an MPP tracker. Several MPP pursuit techniques have been proposed and implemented in recent years
Suligowski, Roman
2014-05-01
Probable Maximum Precipitation based upon the physical mechanisms of precipitation formation at the Kielce Upland. This estimation stems from meteorological analysis of extremely high precipitation events, which occurred in the area between 1961 and 2007 causing serious flooding from rivers that drain the entire Kielce Upland. Meteorological situation has been assessed drawing on the synoptic maps, baric topography charts, satellite and radar images as well as the results of meteorological observations derived from surface weather observation stations. Most significant elements of this research include the comparison between distinctive synoptic situations over Europe and subsequent determination of typical rainfall generating mechanism. This allows the author to identify the source areas of air masses responsible for extremely high precipitation at the Kielce Upland. Analysis of the meteorological situations showed, that the source areas for humid air masses which cause the largest rainfalls at the Kielce Upland are the area of northern Adriatic Sea and the north-eastern coast of the Black Sea. Flood hazard at the Kielce Upland catchments was triggered by daily precipitation of over 60 mm. The highest representative dew point temperature in source areas of warm air masses (these responsible for high precipitation at the Kielce Upland) exceeded 20 degrees Celsius with a maximum of 24.9 degrees Celsius while precipitable water amounted to 80 mm. The value of precipitable water is also used for computation of factors featuring the system, namely the mass transformation factor and the system effectiveness factor. The mass transformation factor is computed based on precipitable water in the feeding mass and precipitable water in the source area. The system effectiveness factor (as the indicator of the maximum inflow velocity and the maximum velocity in the zone of front or ascending currents, forced by orography) is computed from the quotient of precipitable water in
Fast Bayesian inference of optical trap stiffness and particle diffusion
Bera, Sudipta; Paul, Shuvojit; Singh, Rajesh; Ghosh, Dipanjan; Kundu, Avijit; Banerjee, Ayan; Adhikari, R.
2017-01-01
Bayesian inference provides a principled way of estimating the parameters of a stochastic process that is observed discretely in time. The overdamped Brownian motion of a particle confined in an optical trap is generally modelled by the Ornstein-Uhlenbeck process and can be observed directly in experiment. Here we present Bayesian methods for inferring the parameters of this process, the trap stiffness and the particle diffusion coefficient, that use exact likelihoods and sufficient statistics to arrive at simple expressions for the maximum a posteriori estimates. This obviates the need for Monte Carlo sampling and yields methods that are both fast and accurate. We apply these to experimental data and demonstrate their advantage over commonly used non-Bayesian fitting methods.
Fast Bayesian inference of optical trap stiffness and particle diffusion
Bera, Sudipta; Singh, Rajesh; Ghosh, Dipanjan; Kundu, Avijit; Banerjee, Ayan; Adhikari, R
2016-01-01
Bayesian inference provides a principled way of estimating the parameters of a stochastic process that is observed discretely in time. The overdamped Brownian motion of a particle confined in an optical trap is generally modelled by the Ornstein-Uhlenbeck process and can be observed directly in experiment. Here we present Bayesian methods for inferring the parameters of this process, the trap stiffness and the particle diffusion coefficient, that use exact likelihoods and sufficient statistics to arrive at simple expressions for the maximum a posteriori estimates. This obviates the need for Monte Carlo sampling and yields methods that are both fast and accurate. We apply these to experimental data and demonstrate their advantage over commonly used non-Bayesian fitting methods.
Analysis of Gumbel Model for Software Reliability Using Bayesian Paradigm
Directory of Open Access Journals (Sweden)
Raj Kumar
2012-12-01
Full Text Available In this paper, we have illustrated the suitability of Gumbel Model for software reliability data. The model parameters are estimated using likelihood based inferential procedure: classical as well as Bayesian. The quasi Newton-Raphson algorithm is applied to obtain the maximum likelihood estimates and associated probability intervals. The Bayesian estimates of the parameters of Gumbel model are obtained using Markov Chain Monte Carlo(MCMC simulation method in OpenBUGS(established software for Bayesian analysis using Markov Chain Monte Carlo methods. The R functions are developed to study the statistical properties, model validation and comparison tools of the model and the output analysis of MCMC samples generated from OpenBUGS. Details of applying MCMC to parameter estimation for the Gumbel model are elaborated and a real software reliability data set is considered to illustrate the methods of inference discussed in this paper.
Bayesian Network Assessment Method for Civil Aviation Safety Based on Flight Delays
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Huawei Wang
2013-01-01
Full Text Available Flight delays and safety are the principal contradictions in the sound development of civil aviation. Flight delays often come up and induce civil aviation safety risk simultaneously. Based on flight delays, the random characteristics of civil aviation safety risk are analyzed. Flight delays have been deemed to a potential safety hazard. The change rules and characteristics of civil aviation safety risk based on flight delays have been analyzed. Bayesian networks (BN have been used to build the aviation operation safety assessment model based on flight delay. The structure and parameters learning of the model have been researched. By using BN model, some airline in China has been selected to assess safety risk of civil aviation. The civil aviation safety risk of BN model has been assessed by GeNIe software. The research results show that flight delay, which increases the safety risk of civil aviation, can be seen as incremental safety risk. The effectiveness and correctness of the model have been tested and verified.
A Bayesian cluster analysis method for single-molecule localization microscopy data.
Griffié, Juliette; Shannon, Michael; Bromley, Claire L; Boelen, Lies; Burn, Garth L; Williamson, David J; Heard, Nicholas A; Cope, Andrew P; Owen, Dylan M; Rubin-Delanchy, Patrick
2016-12-01
Cell function is regulated by the spatiotemporal organization of the signaling machinery, and a key facet of this is molecular clustering. Here, we present a protocol for the analysis of clustering in data generated by 2D single-molecule localization microscopy (SMLM)-for example, photoactivated localization microscopy (PALM) or stochastic optical reconstruction microscopy (STORM). Three features of such data can cause standard cluster analysis approaches to be ineffective: (i) the data take the form of a list of points rather than a pixel array; (ii) there is a non-negligible unclustered background density of points that must be accounted for; and (iii) each localization has an associated uncertainty in regard to its position. These issues are overcome using a Bayesian, model-based approach. Many possible cluster configurations are proposed and scored against a generative model, which assumes Gaussian clusters overlaid on a completely spatially random (CSR) background, before every point is scrambled by its localization precision. We present the process of generating simulated and experimental data that are suitable to our algorithm, the analysis itself, and the extraction and interpretation of key cluster descriptors such as the number of clusters, cluster radii and the number of localizations per cluster. Variations in these descriptors can be interpreted as arising from changes in the organization of the cellular nanoarchitecture. The protocol requires no specific programming ability, and the processing time for one data set, typically containing 30 regions of interest, is ∼18 h; user input takes ∼1 h.
Directory of Open Access Journals (Sweden)
Nataša Papić-Blagojević
2012-04-01
Full Text Available Marketing approach is associated to market conditions and achieving long term profitability of a company by satisfying consumers’ needs. This approach in tourism does not have to be related only to promoting one touristic destination, but is associated to relation between travel agency and its clients too. It considers that travel agencies adjust their offers to their clients’ needs. In that sense, it is important to analyze the behavior of tourists in the earlier periods with consideration of their preferences. Using Bayesian network, it could be graphically displayed the connection between tourists who have similar taste and relationships between them. On the other hand, the analytic hierarchy process (AHP is used to rank tourist attractions, with also relying on past experience. In this paper we examine possible applications of these two models in tourism in Serbia. The example is hypothetical, but it will serve as a base for future research. Three types of tourism are chosen as a representative in Vojvodina: Cultural, Rural and Business tourism, because they are the bright spot of touristic development in this area. Applied on these forms, analytic hierarchy process has shown its strength in predicting tourists’ preferences.
A Bayesian Target Predictor Method based on Molecular Pairing Energies estimation.
Oliver, Antoni; Canals, Vincent; Rosselló, Josep L
2017-03-06
Virtual screening (VS) is applied in the early drug discovery phases for the quick inspection of huge molecular databases to identify those compounds that most likely bind to a given drug target. In this context, there is the necessity of the use of compact molecular models for database screening and precise target prediction in reasonable times. In this work we present a new compact energy-based model that is tested for its application to Virtual Screening and target prediction. The model can be used to quickly identify active compounds in huge databases based on the estimation of the molecule's pairing energies. The greatest molecular polar regions along with its geometrical distribution are considered by using a short set of smart energy vectors. The model is tested using similarity searches within the Directory of Useful Decoys (DUD) database. The results obtained are considerably better than previously published models. As a Target prediction methodology we propose the use of a Bayesian Classifier that uses a combination of different active compounds to build an energy-dependent probability distribution function for each target.
Bayesian Methods for Reconstructing Sunspot Numbers Before and During the Maunder Minimum
Travaglini, Guido
2017-01-01
The Maunder Minimum (MM) was an extended period of reduced solar activity in terms of yearly sunspot numbers (SSN) during 1610 - 1715. The reality of this "grand minimum" is generally accepted in the scientific community, but the statistics of the SSN record suggest a need for data reconstruction. The MM data show a nonstandard distribution compared with the entire SSN signal (1610 - 2014). The pattern does not satisfy the weakly stationary solar dynamo approximation, which characterizes many natural events spanning centuries or even millennia, including the Sun and the stars. Over the entire observation period (1610 - 2014), the reported SSN exhibits statistically significant regime switches, departures from autoregressive stationarity, and growing trends. Reconstruction of the SSN during the pre-MM and MM periods is performed using five novel statistical procedures in support of signal analysis. A Bayesian-Monte Carlo backcast technique is found to be most reliable and produces an SSN signal that meets the weak-stationarity requirement. The computed MM signal for this reconstruction does not show a "grand" minimum or even a "semi-grand" minimum.
Uncertainty in Ecohydrological Modeling in an Arid Region Determined with Bayesian Methods.
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Junjun Yang
Full Text Available In arid regions, water resources are a key forcing factor in ecosystem circulation, and soil moisture is the critical link that constrains plant and animal life on the soil surface and underground. Simulation of soil moisture in arid ecosystems is inherently difficult due to high variability. We assessed the applicability of the process-oriented CoupModel for forecasting of soil water relations in arid regions. We used vertical soil moisture profiling for model calibration. We determined that model-structural uncertainty constituted the largest error; the model did not capture the extremes of low soil moisture in the desert-oasis ecotone (DOE, particularly below 40 cm soil depth. Our results showed that total uncertainty in soil moisture prediction was improved when input and output data, parameter value array, and structure errors were characterized explicitly. Bayesian analysis was applied with prior information to reduce uncertainty. The need to provide independent descriptions of uncertainty analysis (UA in the input and output data was demonstrated. Application of soil moisture simulation in arid regions will be useful for dune-stabilization and revegetation efforts in the DOE.
A Bayesian Target Predictor Method based on Molecular Pairing Energies estimation
Oliver, Antoni; Canals, Vincent; Rosselló, Josep L.
2017-03-01
Virtual screening (VS) is applied in the early drug discovery phases for the quick inspection of huge molecular databases to identify those compounds that most likely bind to a given drug target. In this context, there is the necessity of the use of compact molecular models for database screening and precise target prediction in reasonable times. In this work we present a new compact energy-based model that is tested for its application to Virtual Screening and target prediction. The model can be used to quickly identify active compounds in huge databases based on the estimation of the molecule’s pairing energies. The greatest molecular polar regions along with its geometrical distribution are considered by using a short set of smart energy vectors. The model is tested using similarity searches within the Directory of Useful Decoys (DUD) database. The results obtained are considerably better than previously published models. As a Target prediction methodology we propose the use of a Bayesian Classifier that uses a combination of different active compounds to build an energy-dependent probability distribution function for each target.
Fang, Xin; Li, Runkui; Kan, Haidong; Bottai, Matteo; Fang, Fang
2016-01-01
Objective To demonstrate an application of Bayesian model averaging (BMA) with generalised additive mixed models (GAMM) and provide a novel modelling technique to assess the association between inhalable coarse particles (PM10) and respiratory mortality in time-series studies. Design A time-series study using regional death registry between 2009 and 2010. Setting 8 districts in a large metropolitan area in Northern China. Participants 9559 permanent residents of the 8 districts who died of respiratory diseases between 2009 and 2010. Main outcome measures Per cent increase in daily respiratory mortality rate (MR) per interquartile range (IQR) increase of PM10 concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NOx, CO) models. Results The Bayesian model averaged GAMM (GAMM+BMA) and the optimal GAMM of PM10, multipollutants and principal components (PCs) of multipollutants showed comparable results for the effect of PM10 on daily respiratory MR, that is, one IQR increase in PM10 concentration corresponded to 1.38% vs 1.39%, 1.81% vs 1.83% and 0.87% vs 0.88% increase, respectively, in daily respiratory MR. However, GAMM+BMA gave slightly but noticeable wider CIs for the single-pollutant model (−1.09 to 4.28 vs −1.08 to 3.93) and the PCs-based model (−2.23 to 4.07 vs −2.03 vs 3.88). The CIs of the multiple-pollutant model from two methods are similar, that is, −1.12 to 4.85 versus −1.11 versus 4.83. Conclusions The BMA method may represent a useful tool for modelling uncertainty in time-series studies when evaluating the effect of air pollution on fatal health outcomes. PMID:27531727
Vries, de R.Y.; Briels, W.J.; Feil, D.; Velde, te G.; Baerends, E.J.
1996-01-01
1990 Sakata and Sato applied the maximum entropy method (MEM) to a set of structure factors measured earlier by Saka and Kato with the Pendellösung method. They found the presence of non-nuclear attractors, i.e., maxima in the density between two bonded atoms. We applied the MEM to a limited set of
Murata, T; Sato, T; Nakamura, S X
2016-01-01
The maximum entropy method is examined as a new tool for solving the ill-posed inversion problem involved in the Lorentz integral transformation (LIT) method. As an example, we apply the method to the spin-dipole strength function of 4He. We show that the method can be successfully used for inversion of LIT, provided the LIT function is available with a sufficient accuracy.
On the maximum-entropy method for kinetic equation of radiation, particle and gas
Energy Technology Data Exchange (ETDEWEB)
El-Wakil, S.A. [Mansoura Univ. (Egypt). Phys. Dept.; Madkour, M.A. [Mansoura Univ. (Egypt). Phys. Dept.; Degheidy, A.R. [Mansoura Univ. (Egypt). Phys. Dept.; Machali, H.M. [Mansoura Univ. (Egypt). Phys. Dept.
1995-02-01
The maximum-entropy approach is used to calculate some problems in radiative transfer and reactor physics such as the escape probability, the emergent and transmitted intensities for a finite slab as well as the emergent intensity for a semi-infinite medium. Also, it is employed to solve problems involving spherical geometry, such as luminosity (the total energy emitted by a sphere), neutron capture probability and the albedo problem. The technique is also employed in the kinetic theory of gases to calculate the Poiseuille flow and thermal creep of a rarefied gas between two plates. Numerical calculations are achieved and compared with the published data. The comparisons demonstrate that the maximum-entropy results are good in agreement with the exact ones. (orig.).
Resende Rosangela Maria Simeão; Jank Liana; Valle Cacilda Borges do; Bonato Ana Lídia Variani
2004-01-01
The objectives of this work were to estimate the genetic and phenotypic parameters and to predict the genetic and genotypic values of the selection candidates obtained from intraspecific crosses in Panicum maximum as well as the performance of the hybrid progeny of the existing and projected crosses. Seventy-nine intraspecific hybrids obtained from artificial crosses among five apomictic and three sexual autotetraploid individuals were evaluated in a clonal test with two replications and ten ...
Movahednejad, E.; Ommi, F.; Hosseinalipour, S. M.; Chen, C. P.; Mahdavi, S. A.
2011-12-01
This paper describes the implementation of the instability analysis of wave growth on liquid jet surface, and maximum entropy principle (MEP) for prediction of droplet diameter distribution in primary breakup region. The early stage of the primary breakup, which contains the growth of wave on liquid-gas interface, is deterministic; whereas the droplet formation stage at the end of primary breakup is random and stochastic. The stage of droplet formation after the liquid bulk breakup can be modeled by statistical means based on the maximum entropy principle. The MEP provides a formulation that predicts the atomization process while satisfying constraint equations based on conservations of mass, momentum and energy. The deterministic aspect considers the instability of wave motion on jet surface before the liquid bulk breakup using the linear instability analysis, which provides information of the maximum growth rate and corresponding wavelength of instabilities in breakup zone. The two sub-models are coupled together using momentum source term and mean diameter of droplets. This model is also capable of considering drag force on droplets through gas-liquid interaction. The predicted results compared favorably with the experimentally measured droplet size distributions for hollow-cone sprays.
Bayesian Inference on Gravitational Waves
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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.
Rius, Jordi
2006-09-01
The maximum-likelihood method is applied to direct methods to derive a more general probability density function of the triple-phase sums which is capable of predicting negative values. This study also proves that maximization of the origin-free modulus sum function S yields, within the limitations imposed by the assumed approximations, the maximum-likelihood estimates of the phases. It thus represents the formal theoretical justification of the S function that was initially derived from Patterson-function arguments [Rius (1993). Acta Cryst. A49, 406-409].
Institute of Scientific and Technical Information of China (English)
GUAN Wenjiang; TANG Lin; ZHU Jiangfeng; TIAN Siquan; XU Liuxiong
2016-01-01
It is widely recognized that assessments of the status of data-poor fish stocks are challenging and that Bayesian analysis is one of the methods which can be used to improve the reliability of stock assessments in data-poor situations through borrowing strength from prior information deduced from species with good-quality data or other known information. Because there is considerable uncertainty remaining in the stock assessment of albacore tuna (Thunnus alalunga) in the Indian Ocean due to the limited and low-quality data, we investigate the advantages of a Bayesian method in data-poor stock assessment by using Indian Ocean albacore stock assessment as an example. Eight Bayesian biomass dynamics models with different prior assumptions and catch data series were developed to assess the stock. The results show (1) the rationality of choice of catch data series and assumption of parameters could be enhanced by analyzing the posterior distribution of the parameters; (2) the reliability of the stock assessment could be improved by using demographic methods to construct a prior for the intrinsic rate of increase (r). Because we can make use of more information to improve the rationality of parameter estimation and the reliability of the stock assessment compared with traditional statistical methods by incorporating any available knowledge into the informative priors and analyzing the posterior distribution based on Bayesian framework in data-poor situations, we suggest that the Bayesian method should be an alternative method to be applied in data-poor species stock assessment, such as Indian Ocean albacore.
2011-01-01
lognormal distribution is zero , the maximum possible value is + ∞, and the parameter values (mean and standard deviation) must be greater than zero . These...and referenced Steve Hora , who has been repeatedly quoted for his argument of “three and seldom more than six” is sufficient ( Hora , 2004, p. 5; K...overconfidence of the experts as discussed by Hora (2004), and errors of estimation as discussed by Winkler (1981). In the case of the Cockpit-21 project
Directory of Open Access Journals (Sweden)
Resende Rosangela Maria Simeão
2004-01-01
Full Text Available The objectives of this work were to estimate the genetic and phenotypic parameters and to predict the genetic and genotypic values of the selection candidates obtained from intraspecific crosses in Panicum maximum as well as the performance of the hybrid progeny of the existing and projected crosses. Seventy-nine intraspecific hybrids obtained from artificial crosses among five apomictic and three sexual autotetraploid individuals were evaluated in a clonal test with two replications and ten plants per plot. Green matter yield, total and leaf dry matter yields and leaf percentage were evaluated in five cuts per year during three years. Genetic parameters were estimated and breeding and genotypic values were predicted using the restricted maximum likelihood/best linear unbiased prediction procedure (REML/BLUP. The dominant genetic variance was estimated by adjusting the effect of full-sib families. Low magnitude individual narrow sense heritabilities (0.02-0.05, individual broad sense heritabilities (0.14-0.20 and repeatability measured on an individual basis (0.15-0.21 were obtained. Dominance effects for all evaluated characteristics indicated that breeding strategies that explore heterosis must be adopted. Less than 5% increase in the parameter repeatability was obtained for a three-year evaluation period and may be the criterion to determine the maximum number of years of evaluation to be adopted, without compromising gain per cycle of selection. The identification of hybrid candidates for future cultivars and of those that can be incorporated into the breeding program was based on the genotypic and breeding values, respectively. The prediction of the performance of the hybrid progeny, based on the breeding values of the progenitors, permitted the identification of the best crosses and indicated the best parents to use in crosses.
DEFF Research Database (Denmark)
Madsen, Henrik; Pearson, Charles P.; Rosbjerg, Dan
1997-01-01
Two regional estimation schemes, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto (GP) distribution for modeling threshold exceedances corresponding to a generalized extreme value (GEV) distribution...... for annual maxima. First, the accuracy of PDS/GP and AMS/GEV regional index-flood T-year event estimators are compared using Monte Carlo simulations. For estimation in typical regions assuming a realistic degree of heterogeneity, the PDS/GP index-flood model is more efficient. The regional PDS and AMS...
Nuclear Enhanced X-ray Maximum Entropy Method Used to Analyze Local Distortions in Simple Structures
DEFF Research Database (Denmark)
Christensen, Sebastian; Bindzus, Niels; Christensen, Mogens
the ideal, undistorted rock-salt structure. NEXMEM employs a simple procedure to normalize extracted structure factors to the atomic form factors. The NDD is reconstructed by performing maximum entropy calculations on the normalized structure factors. NEXMEM has been validated by testing against simulated....... In addition, we have applied NEXMEM to multi-temperature synchrotron powder X-ray diffraction collected on PbX. Based on powder diffraction data, our study demonstrates that NEXMEM successfully improves the atomic resolution over standard MEM. This new tool aids our understanding of the local distortions...
Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants
Jin, Ick Hoon
2014-03-01
Statistical inference for the models with intractable normalizing constants has attracted much attention. During the past two decades, various approximation- or simulation-based methods have been proposed for the problem, such as the Monte Carlo maximum likelihood method and the auxiliary variable Markov chain Monte Carlo methods. The Bayesian stochastic approximation Monte Carlo algorithm specifically addresses this problem: It works by sampling from a sequence of approximate distributions with their average converging to the target posterior distribution, where the approximate distributions can be achieved using the stochastic approximation Monte Carlo algorithm. A strong law of large numbers is established for the Bayesian stochastic approximation Monte Carlo estimator under mild conditions. Compared to the Monte Carlo maximum likelihood method, the Bayesian stochastic approximation Monte Carlo algorithm is more robust to the initial guess of model parameters. Compared to the auxiliary variable MCMC methods, the Bayesian stochastic approximation Monte Carlo algorithm avoids the requirement for perfect samples, and thus can be applied to many models for which perfect sampling is not available or very expensive. The Bayesian stochastic approximation Monte Carlo algorithm also provides a general framework for approximate Bayesian analysis. © 2012 Elsevier B.V. All rights reserved.
Center, Julian L.; Knuth, Kevin H.
2011-03-01
Visual odometry refers to tracking the motion of a body using an onboard vision system. Practical visual odometry systems combine the complementary accuracy characteristics of vision and inertial measurement units. The Mars Exploration Rovers, Spirit and Opportunity, used this type of visual odometry. The visual odometry algorithms in Spirit and Opportunity were based on Bayesian methods, but a number of simplifying approximations were needed to deal with onboard computer limitations. Furthermore, the allowable motion of the rover had to be severely limited so that computations could keep up. Recent advances in computer technology make it feasible to implement a fully Bayesian approach to visual odometry. This approach combines dense stereo vision, dense optical flow, and inertial measurements. As with all true Bayesian methods, it also determines error bars for all estimates. This approach also offers the possibility of using Micro-Electro Mechanical Systems (MEMS) inertial components, which are more economical, weigh less, and consume less power than conventional inertial components.
Bayesian least squares deconvolution
Asensio Ramos, A.; Petit, P.
2015-11-01
Aims: We develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods: We consider LSD under the Bayesian framework and we introduce a flexible Gaussian process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results: We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
Bayesian least squares deconvolution
Ramos, A Asensio
2015-01-01
Aims. To develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods. We consider LSD under the Bayesian framework and we introduce a flexible Gaussian Process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results. We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
The NIFTY way of Bayesian signal inference
Energy Technology Data Exchange (ETDEWEB)
Selig, Marco, E-mail: mselig@mpa-Garching.mpg.de [Max Planck Institut für Astrophysik, Karl-Schwarzschild-Straße 1, D-85748 Garching, Germany, and Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, D-80539 München (Germany)
2014-12-05
We introduce NIFTY, 'Numerical Information Field Theory', a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTY as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D{sup 3}PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.
Soufan, Othman
2016-01-01
Abstract Background Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, methods and solutions proposed to-date, did not completely overcome these challenges. This study is based on a multi-label classification (MLC) technique for modeling correlations between several HTS assays, meaning that a single prediction represents a subset of assigned correlated labels instead of one label. Thus, the devised method provides an increased probability for more accurate predictions of compounds that were not tested in particular assays. Results Here we present DRABAL, a novel MLC solution that incorporates structure learning of a Bayesian network as a step to model dependency between the HTS assays. In this study, DRABAL was used to process more than 1.4 million interactions of over 400,000 compounds and analyze the existing relationships between five large HTS assays from the PubChem BioAssay Database. Compared to different MLC methods, DRABAL significantly improves the F1Score by about 22%, on average. We further illustrated usefulness and utility of DRABAL through screening FDA approved drugs and reported ones that have a high probability to interact with several targets, thus enabling drug-multi-target repositioning. Specifically DRABAL suggests the Thiabendazole drug as a common activator of the NCP1 and Rab-9A proteins, both of which are designed to identify treatment modalities for the Niemannâ Pick type C disease. Conclusion We developed a novel MLC solution based on a Bayesian active learning framework to overcome the challenge of lacking fully labeled training data and exploit actual dependencies between the HTS assays. The solution is motivated by the need to model dependencies between
Bernardo, Jose M
2000-01-01
This highly acclaimed text, now available in paperback, provides a thorough account of key concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development of the theory, which provides, in particular, a detailed discussion of the problem of specification of so-called prior ignorance . The work is written from the authors s committed Bayesian perspective, but an overview of non-Bayesian theories is also provided, and each chapter contains a wide-ranging critica
A BAYESIAN METHOD FOR THE ANALYSIS OF THE DUST EMISSION IN THE FAR-INFRARED AND SUBMILLIMETER
Energy Technology Data Exchange (ETDEWEB)
Veneziani, M.; Noriega-Crespo, A.; Carey, S.; Paladini, R. [Infrared Processing and Analysis Center, California Institute of Technology, Pasadena, CA 91125 (United States); Piacentini, F. [Dipartimento di Fisica, Universita di Roma ' ' La Sapienza' ' , I-00185 Rome (Italy); Paradis, D., E-mail: marcella.veneziani@ipac.caltech.edu [Universite de Toulouse, UPS-OMP, IRAP, F-31062 Toulouse (France)
2013-07-20
We present a method, based on Bayesian statistics, to fit the dust emission parameters in the far-infrared and submillimeter wavelengths. The method estimates the dust temperature and spectral emissivity index, plus their relationship, properly taking into account the statistical and systematic uncertainties. We test it on three sets of simulated sources detectable by the Herschel Space Observatory in the PACS and SPIRE spectral bands (70-500 {mu}m), spanning over a wide range of dust temperatures. The simulated observations are a one-component interstellar medium and two two-component sources, both warm (H II regions) and cold (cold clumps (CCs)). We first define a procedure to identify the better model, then we recover the parameters of the model and measure their physical correlations by means of a Markov Chain Monte Carlo algorithm adopting multi-variate Gaussian priors. In this process, we assess the reliability of the model recovery and of parameter estimation. We conclude that the model and parameters are properly recovered only under certain circumstances and that false models may be derived in some cases. We applied the method to a set of 91 starless CCs in an interarm region of the Galactic plane with low star formation activity, observed by Herschel in the Hi-GAL survey. Our results are consistent with a temperature-independent spectral index.
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Carolina L. Pometti
2014-01-01
Full Text Available Bayesian clustering as implemented in STRUCTURE or GENELAND software is widely used to form genetic groups of populations or individuals. On the other hand, in order to satisfy the need for less computer-intensive approaches, multivariate analyses are specifically devoted to extracting information from large datasets. In this paper, we report the use of a dataset of AFLP markers belonging to 15 sampling sites of Acacia caven for studying the genetic structure and comparing the consistency of three methods: STRUCTURE, GENELAND and DAPC. Of these methods, DAPC was the fastest one and showed accuracy in inferring the K number of populations (K = 12 using the find.clusters option and K = 15 with a priori information of populations. GENELAND in turn, provides information on the area of membership probabilities for individuals or populations in the space, when coordinates are specified (K = 12. STRUCTURE also inferred the number of K populations and the membership probabilities of individuals based on ancestry, presenting the result K = 11 without prior information of populations and K = 15 using the LOCPRIOR option. Finally, in this work all three methods showed high consistency in estimating the population structure, inferring similar numbers of populations and the membership probabilities of individuals to each group, with a high correlation between each other.
Pometti, Carolina L; Bessega, Cecilia F; Saidman, Beatriz O; Vilardi, Juan C
2014-03-01
Bayesian clustering as implemented in STRUCTURE or GENELAND software is widely used to form genetic groups of populations or individuals. On the other hand, in order to satisfy the need for less computer-intensive approaches, multivariate analyses are specifically devoted to extracting information from large datasets. In this paper, we report the use of a dataset of AFLP markers belonging to 15 sampling sites of Acacia caven for studying the genetic structure and comparing the consistency of three methods: STRUCTURE, GENELAND and DAPC. Of these methods, DAPC was the fastest one and showed accuracy in inferring the K number of populations (K = 12 using the find.clusters option and K = 15 with a priori information of populations). GENELAND in turn, provides information on the area of membership probabilities for individuals or populations in the space, when coordinates are specified (K = 12). STRUCTURE also inferred the number of K populations and the membership probabilities of individuals based on ancestry, presenting the result K = 11 without prior information of populations and K = 15 using the LOCPRIOR option. Finally, in this work all three methods showed high consistency in estimating the population structure, inferring similar numbers of populations and the membership probabilities of individuals to each group, with a high correlation between each other.
Directory of Open Access Journals (Sweden)
Bo Ye
2014-01-01
Full Text Available Accurate evaluation and characterization of defects in multilayered structures from eddy current nondestructive testing (NDT signals are a difficult inverse problem. There is scope for improving the current methods used for solving the inverse problem by incorporating information of uncertainty in the inspection process. Here, we propose to evaluate defects quantitatively from eddy current NDT signals using Bayesian networks (BNs. BNs are a useful method in handling uncertainty in the inspection process, eventually leading to the more accurate results. The domain knowledge and the experimental data are used to generate the BN models. The models are applied to predict the signals corresponding to different defect characteristic parameters or to estimate defect characteristic parameters from eddy current signals in real time. Finally, the estimation results are analyzed. Compared to the least squares regression method, BNs are more robust with higher accuracy and have the advantage of being a bidirectional inferential mechanism. This approach allows results to be obtained in the form of full marginal conditional probability distributions, providing more information on the defect. The feasibility of BNs presented and discussed in this paper has been validated.
An electromagnetism-like method for the maximum set splitting problem
Directory of Open Access Journals (Sweden)
Kratica Jozef
2013-01-01
Full Text Available In this paper, an electromagnetism-like approach (EM for solving the maximum set splitting problem (MSSP is applied. Hybrid approach consisting of the movement based on the attraction-repulsion mechanisms combined with the proposed scaling technique directs EM to promising search regions. Fast implementation of the local search procedure additionally improves the efficiency of overall EM system. The performance of the proposed EM approach is evaluated on two classes of instances from the literature: minimum hitting set and Steiner triple systems. The results show, except in one case, that EM reaches optimal solutions up to 500 elements and 50000 subsets on minimum hitting set instances. It also reaches all optimal/best-known solutions for Steiner triple systems.
Directory of Open Access Journals (Sweden)
Lihui Guo
2015-01-01
Full Text Available With the increasing penetration of wind power, the randomness and volatility of wind power output would have a greater impact on safety and steady operation of power system. In allusion to the uncertainty of wind speed and load demand, this paper applied box set robust optimization theory in determining the maximum allowable installed capacity of wind farm, while constraints of node voltage and line capacity are considered. Optimized duality theory is used to simplify the model and convert uncertainty quantities in constraints into certainty quantities. Under the condition of multi wind farms, a bilevel optimization model to calculate penetration capacity is proposed. The result of IEEE 30-bus system shows that the robust optimization model proposed in the paper is correct and effective and indicates that the fluctuation range of wind speed and load and the importance degree of grid connection point of wind farm and load point have impact on the allowable capacity of wind farm.
Directory of Open Access Journals (Sweden)
Ándonios D. Tsolakis
2011-01-01
Full Text Available Problem statement: Main purpose of this study was to investigation toothed gear loading problems using the Finite Element Method. Approach: We used Niemann's equations to compare maximum bending stress which was developed at critical gear-tooth flank point during gear meshing, applied for three distinct spur-gear sizes, each having different teeth number, module and power rating. Results: The results emerging after the application of Niemann's equations were compared to the results derived by application of the Finite Element Method (FEM for the same gear-loading input data. Results are quite satisfactory, since von Mises' equivalent stresses calculated with FEM are of the same order with the results of classical analytical method. Conclusion: Judging from the emerging results, deviation of the two methods, analytical (Niemann's equations and computational (FEM, referring to maximum bending stress is fairly slight, independently of the applied geometrical and loading data of each gear.
Villalba, Jesús
2015-01-01
In this document we are going to derive the equations needed to implement a Variational Bayes estimation of the parameters of the simplified probabilistic linear discriminant analysis (SPLDA) model. This can be used to adapt SPLDA from one database to another with few development data or to implement the fully Bayesian recipe. Our approach is similar to Bishop's VB PPCA.
Directory of Open Access Journals (Sweden)
Krzysztof Tomanek
2014-05-01
Full Text Available The purpose of this article is to present the basic methods for classifying text data. These methods make use of achievements earned in areas such as: natural language processing, the analysis of unstructured data. I introduce and compare two analytical techniques applied to text data. The first analysis makes use of thematic vocabulary tool (sentiment analysis. The second technique uses the idea of Bayesian classification and applies, so-called, naive Bayes algorithm. My comparison goes towards grading the efficiency of use of these two analytical techniques. I emphasize solutions that are to be used to build dictionary accurate for the task of text classification. Then, I compare supervised classification to automated unsupervised analysis’ effectiveness. These results reinforce the conclusion that a dictionary which has received good evaluation as a tool for classification should be subjected to review and modification procedures if is to be applied to new empirical material. Adaptation procedures used for analytical dictionary become, in my proposed approach, the basic step in the methodology of textual data analysis.
Bayesian Exploratory Factor Analysis
DEFF Research Database (Denmark)
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.;
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corr......This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor......, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates...
Bayesian Exploratory Factor Analysis
DEFF Research Database (Denmark)
Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.
2014-01-01
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor......, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates...
Introduction to Bayesian modelling in dental research.
Gilthorpe, M S; Maddick, I H; Petrie, A
2000-12-01
To explain the concepts and application of Bayesian modelling and how it can be applied to the analysis of dental research data. Methodological in nature, this article introduces Bayesian modelling through hypothetical dental examples. The synthesis of RCT results with previous evidence, including expert opinion, is used to illustrate full Bayesian modelling. Meta-analysis, in the form of empirical Bayesian modelling, is introduced. An example of full Bayesian modelling is described for the synthesis of evidence from several studies that investigate the success of root canal treatment. Hierarchical (Bayesian) modelling is demonstrated for a survey of childhood caries, where surface data is nested within subjects. Bayesian methods enhance interpretation of research evidence through the synthesis of information from multiple sources. Bayesian modelling is now readily accessible to clinical researchers and is able to augment the application of clinical decision making in the development of guidelines and clinical practice.
Fatigue life prediction method for contact wire using maximum local stress
Energy Technology Data Exchange (ETDEWEB)
Kim, Yong Seok; Haochuang, Li; Seok, Chang Sung; Koo, Jae Mean [Sungkyunkwan University, Suwon (Korea, Republic of); Lee, Ki Won; Kwon, Sam Young; Cho, Yong Hyeon [Korea Railroad Research Institute, Uiwang (Korea, Republic of)
2015-01-15
Railway contact wires supplying electricity to trains are exposed to repeated mechanical strain and stress caused by their own weight and discontinuous contact with a pantograph during train operation. Since the speed of railway transportation has increased continuously, railway industries have recently reported a number of contact wire failures caused by mechanical fatigue fractures instead of normal wear, which has been a more common failure mechanism. To secure the safety and durability of contact wires in environments with increased train speeds, a bending fatigue test on contact wire has been performed. The test equipment is too complicated to evaluate the fatigue characteristics of contact wire. Thus, the axial tension fatigue test was performed for a standard specimen, and the bending fatigue life for the contact wire structure was then predicted using the maximum local stress occurring at the top of the contact wire. Lastly, the tested bending fatigue life of the structure was compared with the fatigue life predicted by the axial tension fatigue test for verification.
Lussana, C.
2013-04-01
The presented work focuses on the investigation of gridded daily minimum (TN) and maximum (TX) temperature probability density functions (PDFs) with the intent of both characterising a region and detecting extreme values. The empirical PDFs estimation procedure has been realised using the most recent years of gridded temperature analysis fields available at ARPA Lombardia, in Northern Italy. The spatial interpolation is based on an implementation of Optimal Interpolation using observations from a dense surface network of automated weather stations. An effort has been made to identify both the time period and the spatial areas with a stable data density otherwise the elaboration could be influenced by the unsettled station distribution. The PDF used in this study is based on the Gaussian distribution, nevertheless it is designed to have an asymmetrical (skewed) shape in order to enable distinction between warming and cooling events. Once properly defined the occurrence of extreme events, it is possible to straightforwardly deliver to the users the information on a local-scale in a concise way, such as: TX extremely cold/hot or TN extremely cold/hot.
Cheng, K F
2006-09-30
Given the biomedical interest in gene-environment interactions along with the difficulties inherent in gathering genetic data from controls, epidemiologists need methodologies that can increase precision of estimating interactions while minimizing the genotyping of controls. To achieve this purpose, many epidemiologists suggested that one can use case-only design. In this paper, we present a maximum likelihood method for making inference about gene-environment interactions using case-only data. The probability of disease development is described by a logistic risk model. Thus the interactions are model parameters measuring the departure of joint effects of exposure and genotype from multiplicative odds ratios. We extend the typical inference method derived under the assumption of independence between genotype and exposure to that under a more general assumption of conditional independence. Our maximum likelihood method can be applied to analyse both categorical and continuous environmental factors, and generalized to make inference about gene-gene-environment interactions. Moreover, the application of this method can be reduced to simply fitting a multinomial logistic model when we have case-only data. As a consequence, the maximum likelihood estimates of interactions and likelihood ratio tests for hypotheses concerning interactions can be easily computed. The methodology is illustrated through an example based on a study about the joint effects of XRCC1 polymorphisms and smoking on bladder cancer. We also give two simulation studies to show that the proposed method is reliable in finite sample situation.
How to combine correlated data sets -- A Bayesian hyperparameter matrix method
Ma, Yin-Zhe
2013-01-01
We construct a statistical method for performing the joint analyses of multiple correlated astronomical data sets, in which the weights of data sets are determined by their own statistical properties. This method is a generalization of the hyperparameter method constructed by \\cite{Lahav00} and \\cite{Hobson02} which was designed to combine independent data sets. The hyperparameter matrix method we present here includes the relevant weights of multiple data sets and mutual correlations, and when the hyperparameters are marginalized over, the parameters of interest are recovered. We define a new "element-wise" product, which greatly simplifies the likelihood function with hyperparameter matrix. We rigorously prove the simplified formula of the joint likelihood and show that it recovers the original hyperparameter method in the limit of no covariance between data sets. We then illustrate the method by applying a classic model of fitting a straight line to two sets of data. We show that the hyperparameter matrix ...
DEFF Research Database (Denmark)
Wang, Dong; Lu, Kaiyuan; Rasmussen, Peter Omand
2015-01-01
The conventional high frequency signal injection method is to superimpose a high frequency voltage signal to the commanded stator voltage before space vector modulation. Therefore, the magnitude of the voltage used for machine torque production is limited. In this paper, a new high frequency...... injection method, in which high frequency signal is generated by shifting the duty cycle between two neighboring switching periods, is proposed. This method allows injecting a high frequency signal at half of the switching frequency without the necessity to sacrifice the machine fundamental voltage...... amplitude. This may be utilized to develop new position estimation algorithm without involving the inductance in the medium to high speed range. As an application example, a developed inductance independent position estimation algorithm using the proposed high frequency injection method is applied to drive...
N3 Bias Field Correction Explained as a Bayesian Modeling Method
DEFF Research Database (Denmark)
Larsen, Christian Thode; Iglesias, Juan Eugenio; Van Leemput, Koen
2014-01-01
Although N3 is perhaps the most widely used method for MRI bias field correction, its underlying mechanism is in fact not well understood. Specifically, the method relies on a relatively heuristic recipe of alternating iterative steps that does not optimize any particular objective function....... In this paper we explain the successful bias field correction properties of N3 by showing that it implicitly uses the same generative models and computational strategies as expectation maximization (EM) based bias field correction methods. We demonstrate experimentally that purely EM-based methods are capable...... of producing bias field correction results comparable to those of N3 in less computation time....
Maximum Entropy Methods as the Bridge Between Microscopic and Macroscopic Theory
Taylor, Jamie M.
2016-09-01
This paper is concerned with an investigation into a function of macroscopic variables known as the singular potential, building on previous work by Ball and Majumdar. The singular potential is a function of the admissible statistical averages of probability distributions on a state space, defined so that it corresponds to the maximum possible entropy given known observed statistical averages, although non-classical entropy-like objective functions will also be considered. First the set of admissible moments must be established, and under the conditions presented in this work the set is open, bounded and convex allowing a description in terms of supporting hyperplanes, which provides estimates on the development of singularities for related probability distributions. Under appropriate conditions it is shown that the singular potential is strictly convex, as differentiable as the microscopic entropy, and blows up uniformly as the macroscopic variable tends to the boundary of the set of admissible moments. Applications of the singular potential are then discussed, and particular consideration will be given to certain free-energy functionals typical in mean-field theory, demonstrating an equivalence between certain microscopic and macroscopic free-energy functionals. This allows statements about L^1-local minimisers of Onsager's free energy to be obtained which cannot be given by two-sided variations, and overcomes the need to ensure local minimisers are bounded away from zero and +∞ before taking L^∞ variations. The analysis also permits the definition of a dual order parameter for which Onsager's free energy allows an explicit representation. Also, the difficulties in approximating the singular potential by everywhere defined functions, in particular by polynomial functions, are addressed, with examples demonstrating the failure of the Taylor approximation to preserve relevant shape properties of the singular potential.
Directory of Open Access Journals (Sweden)
Rui A. P. Perdigão
2012-06-01
Full Text Available The application of the Maximum Entropy (ME principle leads to a minimum of the Mutual Information (MI, I(X,Y, between random variables X,Y, which is compatible with prescribed joint expectations and given ME marginal distributions. A sequence of sets of joint constraints leads to a hierarchy of lower MI bounds increasingly approaching the true MI. In particular, using standard bivariate Gaussian marginal distributions, it allows for the MI decomposition into two positive terms: the Gaussian MI (I_{g}, depending upon the Gaussian correlation or the correlation between ‘Gaussianized variables’, and a non‑Gaussian MI (I_{ng}, coinciding with joint negentropy and depending upon nonlinear correlations. Joint moments of a prescribed total order p are bounded within a compact set defined by Schwarz-like inequalities, where I_{ng} grows from zero at the ‘Gaussian manifold’ where moments are those of Gaussian distributions, towards infinity at the set’s boundary where a deterministic relationship holds. Sources of joint non-Gaussianity have been systematized by estimating I_{ng} between the input and output from a nonlinear synthetic channel contaminated by multiplicative and non-Gaussian additive noises for a full range of signal-to-noise ratio (snr variances. We have studied the effect of varying snr on I_{g} and I_{ng} under several signal/noise scenarios.
Directory of Open Access Journals (Sweden)
Carlos A. L. Pires
2013-02-01
Full Text Available The Minimum Mutual Information (MinMI Principle provides the least committed, maximum-joint-entropy (ME inferential law that is compatible with prescribed marginal distributions and empirical cross constraints. Here, we estimate MI bounds (the MinMI values generated by constraining sets Tcr comprehended by mcr linear and/or nonlinear joint expectations, computed from samples of N iid outcomes. Marginals (and their entropy are imposed by single morphisms of the original random variables. N-asymptotic formulas are given both for the distribution of cross expectation’s estimation errors, the MinMI estimation bias, its variance and distribution. A growing Tcr leads to an increasing MinMI, converging eventually to the total MI. Under N-sized samples, the MinMI increment relative to two encapsulated sets Tcr1 ⊂ Tcr2 (with numbers of constraints mcr1
Institute of Scientific and Technical Information of China (English)
桑燕芳; 王中根; 刘昌明
2013-01-01
For solving the two difficult problems about Bayesian factor computation (i. e., determination of parameters prior distribution and numerical integration of hydrologic frequency model) in the process of hydrologic frequency model selections and averages, this paper firstly combined the Bayesian sampling method with POME (principle of maximum entropy) to determine the expression of parameters posterior distribution, and then the method of approximate sums after interval segmentation was used instead of the numerical integration of hydrologic frequency model, finally a new method of Bayesian factor computation was proposed. The accuracy and effectiveness of the new method have been verified by both observed hydrologic series analyses and Monte-Carlo tests. The results show that series length and parameters values have great influences on the computation results of Bayesian factor and models posterior probability. The essence of Bayesian information criterion (BIC) is to compute the Bayesian factor by using a group of best parameters estimation results; whenever unfavorable factors are encountered, the analyses results of BIC would become bad due to the inaccurate parameters estimation results. Compared with the BIC criterion, the new method is more effective and reliable, since it can overcome the influence of those unfavorable factors by analyzing and describing the uncertainties of model parameters.%为克服水文频率线型选择和综合过程中,贝叶斯因子求解时参数先验分布确定和线型边缘分布数值积分这两个难点问题,联合应用了贝叶斯采样方法和最大熵原理(POME)求解参数后验分布表达式,然后应用参数采样结果中逐个样本近似求和方法代替线型边缘分布积分过程,进而建立了贝叶斯因子求解新方法.实例分析和Monte-Carlo统计试验验证了该方法的准确性和有效性.分析结果显示:序列长度和参数取值大小等因素对贝叶斯因子和水文线型后
Bayesian inference with ecological applications
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...
How to combine correlated data sets-A Bayesian hyperparameter matrix method
Ma, Y.-Z.; Berndsen, A.
2014-07-01
We construct a “hyperparameter matrix” statistical method for performing the joint analyses of multiple correlated astronomical data sets, in which the weights of data sets are determined by their own statistical properties. This method is a generalization of the hyperparameter method constructed by Lahav et al. (2000) and Hobson et al. (2002) which was designed to combine independent data sets. The advantage of our method is to treat correlations between multiple data sets and gives appropriate relevant weights of multiple data sets with mutual correlations. We define a new “element-wise” product, which greatly simplifies the likelihood function with hyperparameter matrix. We rigorously prove the simplified formula of the joint likelihood and show that it recovers the original hyperparameter method in the limit of no covariance between data sets. We then illustrate the method by applying it to a demonstrative toy model of fitting a straight line to two sets of data. We show that the hyperparameter matrix method can detect unaccounted systematic errors or underestimated errors in the data sets. Additionally, the ratio of Bayes' factors provides a distinct indicator of the necessity of including hyperparameters. Our example shows that the likelihood we construct for joint analyses of correlated data sets can be widely applied to many astrophysical systems.
Arish, Saeid; Safari, Hossein; Amiri, Ali
2016-01-01
The solar corona is the origin of very dynamic events that are mostly produced in active regions (AR) and coronal holes (CH). The exact location of these large-scale features can be determined by applying image-processing approaches to extreme-ultraviolet (EUV) data. We here investigate the problem of segmentation of solar EUV images into ARs, CHs, and quiet-Sun (QS) images in a firm Bayesian way. On the basis of Bayes' rule, we need to obtain both prior and likelihood models. To find the prior model of an image, we used a Potts model in non-local mode. To construct the likelihood model, we combined a mixture of a Markov-Gauss model and non-local means. After estimating labels and hyperparameters with the Gibbs estimator, cellular learning automata were employed to determine the label of each pixel. We applied the proposed method to a Solar Dynamics Observatory/ Atmospheric Imaging Assembly (SDO/AIA) dataset recorded during 2011 and found that the mean value of the filling factor of ARs is 0.032 and 0.057 for...
Bayesian Approach for Inconsistent Information.
Stein, M; Beer, M; Kreinovich, V
2013-10-01
In engineering situations, we usually have a large amount of prior knowledge that needs to be taken into account when processing data. Traditionally, the Bayesian approach is used to process data in the presence of prior knowledge. Sometimes, when we apply the traditional Bayesian techniques to engineering data, we get inconsistencies between the data and prior knowledge. These inconsistencies are usually caused by the fact that in the traditional approach, we assume that we know the exact sample values, that the prior distribution is exactly known, etc. In reality, the data is imprecise due to measurement errors, the prior knowledge is only approximately known, etc. So, a natural way to deal with the seemingly inconsistent information is to take this imprecision into account in the Bayesian approach - e.g., by using fuzzy techniques. In this paper, we describe several possible scenarios for fuzzifying the Bayesian approach. Particular attention is paid to the interaction between the estimated imprecise parameters. In this paper, to implement the corresponding fuzzy versions of the Bayesian formulas, we use straightforward computations of the related expression - which makes our computations reasonably time-consuming. Computations in the traditional (non-fuzzy) Bayesian approach are much faster - because they use algorithmically efficient reformulations of the Bayesian formulas. We expect that similar reformulations of the fuzzy Bayesian formulas will also drastically decrease the computation time and thus, enhance the practical use of the proposed methods.
Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.
2016-12-01
Sensitivity analysis has been an important tool in groundwater modeling to identify the influential parameters. Among various sensitivity analysis methods, the variance-based global sensitivity analysis has gained popularity for its model independence characteristic and capability of providing accurate sensitivity measurements. However, the conventional variance-based method only considers uncertainty contribution of single model parameters. In this research, we extended the variance-based method to consider more uncertainty sources and developed a new framework to allow flexible combinations of different uncertainty components. We decompose the uncertainty sources into a hierarchical three-layer structure: scenario, model and parametric. Furthermore, each layer of uncertainty source is capable of containing multiple components. An uncertainty and sensitivity analysis framework was then constructed following this three-layer structure using Bayesian network. Different uncertainty components are represented as uncertain nodes in this network. Through the framework, variance-based sensitivity analysis can be implemented with great flexibility of using different grouping strategies for uncertainty components. The variance-based sensitivity analysis thus is improved to be able to investigate the importance of an extended range of uncertainty sources: scenario, model, and other different combinations of uncertainty components which can represent certain key model system processes (e.g., groundwater recharge process, flow reactive transport process). For test and demonstration purposes, the developed methodology was implemented into a test case of real-world groundwater reactive transport modeling with various uncertainty sources. The results demonstrate that the new sensitivity analysis method is able to estimate accurate importance measurements for any uncertainty sources which were formed by different combinations of uncertainty components. The new methodology can
DEFF Research Database (Denmark)
Albrechtsen, Anders; Castella, Sofie; Andersen, Gitte;
2007-01-01
conditions. We present a new powerful statistical model for analyzing and interpreting genomic data that influence multifactorial phenotypic traits with a complex and likely polygenic inheritance. The new method is based on Markov chain Monte Carlo (MCMC) and allows for identification of sets of SNPs...... and environmental factors that when combined increase disease risk or change the distribution of a quantitative trait. Using simulations, we show that the MCMC method can detect disease association when multiple, interacting SNPs are present in the data. When applying the method on real large-scale data from...
Energy Technology Data Exchange (ETDEWEB)
Cedola, A.P., E-mail: ariel.cedola@ing.unlp.edu.a [Grupo de Estudio de Materiales y Dispositivos Electronicos (GEMyDE), Dpto. Electrotecnia, Facultad de Ingenieria, Universidad Nacional de La Plata, 48 y 116, C.C. 91, La Plata 1900, Buenos Aires (Argentina); Cappelletti, M.A. [Grupo de Estudio de Materiales y Dispositivos Electronicos (GEMyDE), Dpto. Electrotecnia, Facultad de Ingenieria, Universidad Nacional de La Plata, 48 y 116, C.C. 91, La Plata 1900, Buenos Aires (Argentina); Casas, G. [Grupo de Estudio de Materiales y Dispositivos Electronicos (GEMyDE), Dpto. Electrotecnia, Facultad de Ingenieria, Universidad Nacional de La Plata, 48 y 116, C.C. 91, La Plata 1900, Buenos Aires (Argentina); Universidad Nacional de Quilmes, Roque Saenz Pena 352, Bernal 1876, Buenos Aires (Argentina); Peltzer y Blanca, E.L. [Grupo de Estudio de Materiales y Dispositivos Electronicos (GEMyDE), Dpto. Electrotecnia, Facultad de Ingenieria, Universidad Nacional de La Plata, 48 y 116, C.C. 91, La Plata 1900, Buenos Aires (Argentina); Instituto de Fisica de Liquidos y Sistemas Biologicos (IFLYSIB), CONICET - UNLP - CIC, La Plata 1900, Buenos Aires (Argentina)
2011-02-11
An iterative method based on numerical simulations was developed to enhance the proton radiation tolerance and the responsivity of Si PIN photodiodes. The method allows to calculate the optimal values of the intrinsic layer thickness and the incident light wavelength, in function of the light intensity and the maximum proton fluence to be supported by the device. These results minimize the effects of radiation on the total reverse current of the photodiode and maximize its response to light. The implementation of the method is useful in the design of devices whose operation point should not suffer variations due to radiation.
Frolova A. O.
2012-01-01
Reverse engineering of gene regulatory networks is an intensively studied topic in Systems Biology as it reconstructs regulatory interactions between all genes in the genome in the most complete form. The extreme computational complexity of this problem and lack of thorough reviews on reconstruction methods of gene regulatory network is a significant obstacle to further development of this area. In this article the two most common methods for modeling gene regulatory networks are surveyed: Bo...
Bayesian approach to inverse statistical mechanics.
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.
Nezhel'skaya, L. A.
2016-09-01
A flow of physical events (photons, electrons, and other elementary particles) is studied. One of the mathematical models of such flows is the modulated MAP flow of events circulating under conditions of unextendable dead time period. It is assumed that the dead time period is an unknown fixed value. The problem of estimation of the dead time period from observations of arrival times of events is solved by the method of maximum likelihood.
Directory of Open Access Journals (Sweden)
Jiaming Liu
2016-01-01
Full Text Available Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA method combined with three statistical downscaling methods, which are support vector machine (SVM, BCC/RCG-Weather Generators (BCC/RCG-WG, and Statistics Downscaling Model (SDSM, is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB. The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.
Brochu, Eric; de Freitas, Nando
2010-01-01
We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences.
Directory of Open Access Journals (Sweden)
Lau Nguyen Dinh
2016-01-01
Full Text Available The problem of finding maximum flow in network graph is extremely interesting and practically applicable in many fields in our daily life, especially in transportation. Therefore, a lot of researchers have been studying this problem in various methods. Especially in 2013, we has developed a new algorithm namely, postflow-pull algorithm to find the maximum flow on traditional networks. In this paper, we revised postflow-push methods to solve this problem of finding maximum flow on extended mixed network. In addition, to take more advantage of multi-core architecture of the parallel computing system, we build this parallel algorithm. This is a completely new method not being announced in the world. The results of this paper are basically systematized and proven. The idea of this algorithm is using multi processors to work in parallel by postflow_push algorithm. Among these processors, there is one main processor managing data, sending data to the sub processors, receiving data from the sub-processors. The sub-processors simultaneously execute their work and send their data to the main processor until the job is finished, the main processor will show the results of the problem.
Blunt, Sarah Caroline; Nielsen, Eric; De Rosa, Robert J.; Konopacky, Quinn M.; Ryan, Dominic; Wang, Jason; Pueyo, Laurent; Rameau, Julien; Marois, Christian; Marchis, Franck; Macintosh, Bruce; Graham, James R.; GPIES Collaboration
2017-01-01
Direct imaging planet-finders like the Gemini Planet Imager (GPI) allow for direct imaging of exoplanets with orbital periods beyond ~10 years that are still close enough to their host stars to undergo detectable orbital motion on year or multi-year timescales, creating a need for methods that rapidly characterize newly discovered planets using relative astrometry covering a short fraction of an orbital period. We address this problem with Orbits for the Impatient (OFTI), a statistically robust and computationally efficient Bayesian rejection sampling method for fitting orbits to astrometric datasets covering small orbital fractions from directly imaged exoplanets, brown dwarfs, and wide-orbit stellar binaries. We demonstrate that OFTI produces valid orbital solutions by directly comparing its outputs with those of two Markov Chain Monte Carlo (MCMC) implementations, and compare the computational speeds of OFTI and MCMC as a function of orbital fraction spanned by input astrometry. We find that for well-sampled orbits with astrometry covering less than 15% of the total orbital period, OFTI converges on the correct orbital solution in orders of magnitude less CPU time than MCMC. Exoplanet observations with space missions such as the WFIRST coronagraph present a similar problem of sparse sampling, and we show how these methods can efficiently constrain the orbital inclination, phase, and separation of a planet such as 47 Uma c. Finally, we present some of the first orbital fits to astrometry from directly imaged exoplanets and brown dwarfs in the literature, including GJ 504 b, CD-35 2722 B, kappa And b, and HR 3549 B.
DEFF Research Database (Denmark)
Burgess, Stephen; Thompson, Simon G; Andrews, G
2010-01-01
Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context o...
Bousserez, Nicolas
2016-01-01
This paper provides a detailed theoretical analysis of methods to approximate the solutions of high-dimensional (>10^6) linear Bayesian problems. An optimal low-rank projection that maximizes the information content of the Bayesian inversion is proposed and efficiently constructed using a scalable randomized SVD algorithm. Useful optimality results are established for the associated posterior error covariance matrix and posterior mean approximations, which are further investigated in a numerical experiment consisting of a large-scale atmospheric tracer transport source-inversion problem. This method proves to be a robust and efficient approach to dimension reduction, as well as a natural framework to analyze the information content of the inversion. Possible extensions of this approach to the non-linear framework in the context of operational numerical weather forecast data assimilation systems based on the incremental 4D-Var technique are also discussed, and a detailed implementation of a new Randomized Incr...
Hedlund, Jonas
2014-01-01
This paper introduces private sender information into a sender-receiver game of Bayesian persuasion with monotonic sender preferences. I derive properties of increasing differences related to the precision of signals and use these to fully characterize the set of equilibria robust to the intuitive criterion. In particular, all such equilibria are either separating, i.e., the sender's choice of signal reveals his private information to the receiver, or fully disclosing, i.e., the outcome of th...
Kirstein, Roland
2005-01-01
This paper presents a modification of the inspection game: The ?Bayesian Monitoring? model rests on the assumption that judges are interested in enforcing compliant behavior and making correct decisions. They may base their judgements on an informative but imperfect signal which can be generated costlessly. In the original inspection game, monitoring is costly and generates a perfectly informative signal. While the inspection game has only one mixed strategy equilibrium, three Perfect Bayesia...
Bayesian structural equation modeling in sport and exercise psychology.
Stenling, Andreas; Ivarsson, Andreas; Johnson, Urban; Lindwall, Magnus
2015-08-01
Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.
Gaffney, Jim A; Sonnad, Vijay; Libby, Stephen B
2013-01-01
The complex nature of inertial confinement fusion (ICF) experiments results in a very large number of experimental parameters that are only known with limited reliability. These parameters, combined with the myriad physical models that govern target evolution, make the reliable extraction of physics from experimental campaigns very difficult. We develop an inference method that allows all important experimental parameters, and previous knowledge, to be taken into account when investigating underlying microphysics models. The result is framed as a modified $\\chi^{2}$ analysis which is easy to implement in existing analyses, and quite portable. We present a first application to a recent convergent ablator experiment performed at the NIF, and investigate the effect of variations in all physical dimensions of the target (very difficult to do using other methods). We show that for well characterised targets in which dimensions vary at the 0.5% level there is little effect, but 3% variations change the results of i...
Maximum likelihood methods for investigating reporting rates of rings on hunter-shot birds
Conroy, M.J.; Morgan, B.J.T.; North, P.M.
1985-01-01
It is well known that hunters do not report 100% of the rings that they find on shot birds. Reward studies can be used to estimate what this reporting rate is, by comparison of recoveries of rings offering a monetary reward, to ordinary rings. A reward study of American Black Ducks (Anas rubripes) is used to illustrate the design, and to motivate the development of statistical models for estimation and for testing hypotheses of temporal and geographic variation in reporting rates. The method involves indexing the data (recoveries) and parameters (reporting, harvest, and solicitation rates) by geographic and temporal strata. Estimates are obtained under unconstrained (e.g., allowing temporal variability in reporting rates) and constrained (e.g., constant reporting rates) models, and hypotheses are tested by likelihood ratio. A FORTRAN program, available from the author, is used to perform the computations.
Maximum forces sustained during various methods of exiting commercial tractors, trailers and trucks.
Fathallah, F A; Cotnam, J P
2000-02-01
Many commercial vehicles have steps and grab-rails to assist the driver in safely entering/exiting the vehicle. However, many drivers do not use these aids. The purpose of this study was to compare impact forces experienced during various exit methods from commercial equipment. The study investigated impact forces of ten male subjects while exiting two tractors, a step-van, a box-trailer, and a cube-van. The results showed that exiting from cab-level or trailer-level resulted in impact forces as high as 12 times the subject's body weight; whereas, fully utilizing the steps and grab-rails resulted in impact forces less than two times body weight. An approach that emphasizes optimal design of entry/exit aids coupled with driver training and education is expected to minimize exit-related injuries.
FPGA Acceleration of the phylogenetic likelihood function for Bayesian MCMC inference methods
Directory of Open Access Journals (Sweden)
Bakos Jason D
2010-04-01
Full Text Available Abstract Background Likelihood (ML-based phylogenetic inference has become a popular method for estimating the evolutionary relationships among species based on genomic sequence data. This method is used in applications such as RAxML, GARLI, MrBayes, PAML, and PAUP. The Phylogenetic Likelihood Function (PLF is an important kernel computation for this method. The PLF consists of a loop with no conditional behavior or dependencies between iterations. As such it contains a high potential for exploiting parallelism using micro-architectural techniques. In this paper, we describe a technique for mapping the PLF and supporting logic onto a Field Programmable Gate Array (FPGA-based co-processor. By leveraging the FPGA's on-chip DSP modules and the high-bandwidth local memory attached to the FPGA, the resultant co-processor can accelerate ML-based methods and outperform state-of-the-art multi-core processors. Results We use the MrBayes 3 tool as a framework for designing our co-processor. For large datasets, we estimate that our accelerated MrBayes, if run on a current-generation FPGA, achieves a 10× speedup relative to software running on a state-of-the-art server-class microprocessor. The FPGA-based implementation achieves its performance by deeply pipelining the likelihood computations, performing multiple floating-point operations in parallel, and through a natural log approximation that is chosen specifically to leverage a deeply pipelined custom architecture. Conclusions Heterogeneous computing, which combines general-purpose processors with special-purpose co-processors such as FPGAs and GPUs, is a promising approach for high-performance phylogeny inference as shown by the growing body of literature in this field. FPGAs in particular are well-suited for this task because of their low power consumption as compared to many-core processors and Graphics Processor Units (GPUs 1.
Yuan, Z; Chappell, R; Bailey, H
2007-03-01
We consider the case of phase I trials for treatment of cancer or other severe diseases in which grade information is available about the severity of toxicity. Most dose allocation procedures dichotomize toxicity grades based on being dose limiting, which may not work well for severe and possibly irreversible toxicities such as renal, liver, and neurological toxicities, or toxicities with long duration. We propose a simple extension to the continual reassessment method (CRM), called the Quasi-CRM, to incorporate grade information. Toxicity grades are first converted to numeric scores that reflect their impacts on the dose allocation procedure, and then incorporated into the CRM using the quasi-Bernoulli likelihood. A simulation study demonstrates that the Quasi-CRM is superior to the standard CRM and comparable to a univariate version of the Bekele and Thall method (2004, Journal of the American Statistical Association 99, 26-35). We also present sensitivity analysis of the new method with respect to toxicity scores, and discuss practical issues such as extending the simple algorithmic up-and-down designs.
Institute of Scientific and Technical Information of China (English)
Haijing Niu; Ping Guo; Xiaodong Song; Tianzi Jiang
2008-01-01
The sensitivity of diffuse optical tomography (DOT) imaging exponentially decreases with the increase of photon penetration depth, which leads to a poor depth resolution for DOT. In this letter, an exponential adjustment method (EAM) based on maximum singular value of layered sensitivity is proposed. Optimal depth resolution can be achieved by compensating the reduced sensitivity in the deep medium. Simulations are performed using a semi-infinite model and the simulation results show that the EAM method can substantially improve the depth resolution of deeply embedded objects in the medium. Consequently, the image quality and the reconstruction accuracy for these objects have been largely improved.
Uchiyama, Takanori; Minamitani, Haruyuki; Sakata, Makoto
1990-01-01
The complex maximum entropy method and complex autoregressive model fitting with the singular value decomposition method (SVD) were applied to the free induction decay signal data obtained with a Fourier transform nuclear magnetic resonance spectrometer to estimate superresolved NMR spectra. The practical estimation of superresolved NMR spectra are shown on the data of phosphorus-31 nuclear magnetic resonance spectra. These methods provide sharp peaks and high signal-to-noise ratio compared with conventional fast Fourier transform. The SVD method was more suitable for estimating superresolved NMR spectra than the MEM because the SVD method allowed high-order estimation without spurious peaks, and it was easy to determine the order and the rank.
Directory of Open Access Journals (Sweden)
Rania, M. Shalaby
2015-10-01
Full Text Available This paper deals with Bayesian and non-Bayesian methods for estimating parameters of the bivariate Pareto (BP distribution based on censored samples are considered with shape parameters λ and known scale parameter β. The maximum likelihood estimators MLE of the unknown parameters are derived. The Bayes estimators are obtained with respect to the squared error loss function and the prior distributions allow for prior dependence among the components of the parameter vector. .Posterior distributions for parameters of interest are derived and their properties are described. If the scale parameter is known, the Bayes estimators of the unknown parameters can be obtained in explicit forms under the assumptions of independent priors. An extensive computer simulation is used to compare the performance of the proposed estimators using MathCAD (14.
Niemi, Jarad; West, Mike
2010-06-01
We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.
A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases
Directory of Open Access Journals (Sweden)
Karp Peter D
2004-06-01
Full Text Available Abstract Background The PathoLogic program constructs Pathway/Genome databases by using a genome's annotation to predict the set of metabolic pathways present in an organism. PathoLogic determines the set of reactions composing those pathways from the enzymes annotated in the organism's genome. Most annotation efforts fail to assign function to 40–60% of sequences. In addition, large numbers of sequences may have non-specific annotations (e.g., thiolase family protein. Pathway holes occur when a genome appears to lack the enzymes needed to catalyze reactions in a pathway. If a protein has not been assigned a specific function during the annotation process, any reaction catalyzed by that protein will appear as a missing enzyme or pathway hole in a Pathway/Genome database. Results We have developed a method that efficiently combines homology and pathway-based evidence to identify candidates for filling pathway holes in Pathway/Genome databases. Our program not only identifies potential candidate sequences for pathway holes, but combines data from multiple, heterogeneous sources to assess the likelihood that a candidate has the required function. Our algorithm emulates the manual sequence annotation process, considering not only evidence from homology searches, but also considering evidence from genomic context (i.e., is the gene part of an operon? and functional context (e.g., are there functionally-related genes nearby in the genome? to determine the posterior belief that a candidate has the required function. The method can be applied across an entire metabolic pathway network and is generally applicable to any pathway database. The program uses a set of sequences encoding the required activity in other genomes to identify candidate proteins in the genome of interest, and then evaluates each candidate by using a simple Bayes classifier to determine the probability that the candidate has the desired function. We achieved 71% precision at a
Directory of Open Access Journals (Sweden)
Olga L. Quintero
Full Text Available Biotechnological processes represent a challenge in the control field, due to their high nonlinearity. In particular, continuous alcoholic fermentation from Zymomonas mobilis (Z.m presents a significant challenge. This bioprocess has high ethanol performance, but it exhibits an oscillatory behavior in process variables due to the influence of inhibition dynamics (rate of ethanol concentration over biomass, substrate, and product concentrations. In this work a new solution for control of biotechnological variables in the fermentation process is proposed, based on numerical methods and linear algebra. In addition, an improvement to a previously reported state estimator, based on particle filtering techniques, is used in the control loop. The feasibility estimator and its performance are demonstrated in the proposed control loop. This methodology makes it possible to develop a controller design through the use of dynamic analysis with a tested biomass estimator in Z.m and without the use of complex calculations.
Subliminal or not? Comparing null-hypothesis and Bayesian methods for testing subliminal priming.
Sand, Anders; Nilsson, Mats E
2016-08-01
A difficulty for reports of subliminal priming is demonstrating that participants who actually perceived the prime are not driving the priming effects. There are two conventional methods for testing this. One is to test whether a direct measure of stimulus perception is not significantly above chance on a group level. The other is to use regression to test if an indirect measure of stimulus processing is significantly above zero when the direct measure is at chance. Here we simulated samples in which we assumed that only participants who perceived the primes were primed by it. Conventional analyses applied to these samples had a very large error rate of falsely supporting subliminal priming. Calculating a Bayes factor for the samples very seldom falsely supported subliminal priming. We conclude that conventional tests are not reliable diagnostics of subliminal priming. Instead, we recommend that experimenters calculate a Bayes factor when investigating subliminal priming.
Aggarwal, Namita; Rana, Bharti; Agrawal, R K; Kumaran, Senthil
2015-01-01
In this paper, we propose a three-phased method for diagnosis of Alzheimer's disease using the structural magnetic resonance imaging (MRI). In first phase, gray matter tissue probability map is obtained from every brain MRI volume. Further, five regions of interest (ROIs) are extracted as per prior knowledge. In second phase, features are extracted from each ROI using 3D dual-tree discrete wavelet transform. In third phase, relevant features are selected using minimum redundancy maximum relevance features selection technique. The decision model is built with features so obtained, using a classifier. To evaluate the effectiveness of the proposed method, experiments are performed with four well-known classifiers on four data sets, built from a publicly available OASIS database. The performance is evaluated in terms of sensitivity, specificity and classification accuracy. It was observed that the proposed method outperforms existing methods in terms of all three performance measures. This is further validated with statistical tests.
Owusu-Edusei, Kwame; Owens, Chantelle J
2009-01-01
Background Chlamydia continues to be the most prevalent disease in the United States. Effective spatial monitoring of chlamydia incidence is important for successful implementation of control and prevention programs. The objective of this study is to apply Bayesian smoothing and exploratory spatial data analysis (ESDA) methods to monitor Texas county-level chlamydia incidence rates by examining spatiotemporal patterns. We used county-level data on chlamydia incidence (for all ages, gender and races) from the National Electronic Telecommunications System for Surveillance (NETSS) for 2004 and 2005. Results Bayesian-smoothed chlamydia incidence rates were spatially dependent both in levels and in relative changes. Erath county had significantly (p 300 cases per 100,000 residents) than its contiguous neighbors (195 or less) in both years. Gaines county experienced the highest relative increase in smoothed rates (173% – 139 to 379). The relative change in smoothed chlamydia rates in Newton county was significantly (p systems over time. PMID:19245686