Download Bayesian Modeling Using Winbugs ebook PDF or Read Online books in PDF, EPUB, and Mobi Format. Click Download or Read Online button to Bayesian Modeling Using Winbugs book pdf for free now.

Author : Ioannis Ntzoufras
ISBN : 9781118210352
Genre : Mathematics
File Size : 83.30 MB
Format : PDF, Kindle
Download : 315
Read : 1124

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.

Author : Ioannis Ntzoufras
ISBN : 0470434546
Genre : Mathematics
File Size : 31.78 MB
Format : PDF, ePub, Docs
Download : 243
Read : 1250

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.

Author : Marc Kéry
ISBN : 9780123870209
Genre : Science
File Size : 55.26 MB
Format : PDF, ePub, Docs
Download : 952
Read : 164

Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist All WinBUGS/OpenBUGS analyses are completely integrated in software R Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R

Author : Marc Kery
ISBN : 0123786061
Genre : Science
File Size : 27.98 MB
Format : PDF, Docs
Download : 333
Read : 686

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance. Introduction to the essential theories of key models used by ecologists Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGS Provides every detail of R and WinBUGS code required to conduct all analyses Companion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)

Author : Dipak K. Dey
ISBN : 0824790340
Genre : Mathematics
File Size : 45.87 MB
Format : PDF, Kindle
Download : 104
Read : 690

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.

Author : Marta Blangiardo
ISBN : 9781118326558
Genre : Mathematics
File Size : 68.27 MB
Format : PDF, ePub, Mobi
Download : 795
Read : 472

Spatial and Spatio–Temporal Bayesian Models with R–INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio–temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R–INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations

Author : Peter Congdon
ISBN : 9781118895061
Genre : Mathematics
File Size : 71.1 MB
Format : PDF, ePub
Download : 307
Read : 644

This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.

Author : Palavinnage Saman Muthukumarana
ISBN : OCLC:897841870
Genre : Bayesian statistical decision theory
File Size : 89.85 MB
Format : PDF, ePub, Mobi
Download : 363
Read : 1034

In Bayesian statistics we are interested in the posterior distribution of parameters. In simple cases we can derive analytical expressions for the posterior. However in most situations, the posterior expectations cannot be calculated analytically due to the complexity of the integrals. This thesis develops some new methodologies for applied problems which deal with multidimensional parameters, complex model structures and complex likelihood functions. The first project is concerned with the simulation of one-day cricket matches. Given that only a finite number of outcomes can occur on each ball, a discrete generator on a finite set is developed where the outcome probabilities are estimated from historical data. The probabilities depend on the batsman, the bowler, the number of wickets lost, the number of balls bowled and the innings. The proposed simulator appears to do a reasonable job at producing realistic results. The simulator allows investigators to address complex questions involving one-day cricket matches. The second project investigates the suitability of Dirichlet process priors in the Bayesian analysis of network data. Dirichlet process priors allow the researcher to weaken prior assumptions by going from a parametric to a semiparametric framework. This is important in the analysis of network data where complex nodal relationships rarely allow a researcher the confidence in assigning parametric priors. The Dirichlet process also provides a clustering mechanism which is often suitable for network data where groups of individuals in a network can be thought of as arising from the same cohort. The approach is highlighted on two network models and implemented using WinBUGS. The third project develops a Bayesian latent variable model to analyze ordinal survey data. The data are viewed as multivariate responses arising from a class of continuous latent variables with known cut-points. Each respondent is characterized by two parameters that have a Dirichlet process as their joint prior distribution. The proposed mechanism adjusts for classes of personality traits. As the resulting posterior distribution is complex and high-dimensional, posterior expectations are approximated by MCMC methods. The methodology is tested through simulation studies and illustrated using student feedback data from course evaluations at Simon Fraser University.

Author : Jim Albert
ISBN : 9780387922980
Genre : Mathematics
File Size : 24.16 MB
Format : PDF, Docs
Download : 591
Read : 723

There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).

Author : Michael D. Lee
ISBN : 9781107653917
Genre : Psychology
File Size : 83.21 MB
Format : PDF, Docs
Download : 576
Read : 240

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.