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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010199917 | QA279.5 N89 2009 | Open Access Book | Book | Searching... |
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Summary
Summary
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 Notes
Ioannis Ntzoufras, PhD, is Assistant Professor of Statistics at Athens University of Economics and Business (Greece). Dr. Ntzoufras has published numerous journal articles in his areas of research interest, which include Bayesian statistics, statistical analysis and programming, and generalized linear models.
Table of Contents
| List of Figures |
| List of Tables |
| Preface |
| Acknowledgments |
| Acronyms |
| 1 Introduction to Bayesian inference |
| 1.1 Introduction: Bayesian modeling in the 21st century |
| 1.2 Definition of statistical models |
| 1.3 Bayes theorem |
| 1.4 Model based Bayesian inference |
| 1.5 Inference using conjugate prior distributions |
| 1.6 Non Conjugate Analysis |
| Problems |
| 2 Markov Chain Monte Carlo Algorithms in Bayesian Inference |
| 2.1 Simulation, Monte Carlo integration and their implementation in Bayesian inference |
| 2.2 Markov chain Monte Carlo methods |
| 2.3 Popular MCMC algorithms |
| 2.4 Summary and closing remarks |
| Problems |
| 3 The WinBUGS software: Introduction, Set-up and Basic Analysis |
| 3.1 Introduction and historical background |
| 3.2 The WinBUGS environment |
| 3.3 Preliminaries on using WinBUGS |
| 3.4 Building Bayesian models in WinBUGS |
| 3.5 Compiling the model and simulating values |
| 3.6 Basic Output analysis using the Sample Monitor Tool |
| 3.7 Summarizing the procedure |
| 3.8 Chapter summary and concluding comments |
| Problems |
| 4 The WinBUGS Software: Illustration, Results and Further Analysis |
| 4.1 A complete example of running MCMC in WinBUGS for a simple model |
| 4.2 Further output analysis using the inference menu |
| 4.3 Multiple chains |
| 4.4 Changing the properties of a figure |
| 4.5 Other tools and menus |
| 4.6 Summary and concluding remarks |
| Problems |
| 5 Introduction to Bayesian models: Normal models |
| 5.1 General modeling principles |
| 5.2 Model specification in Normal regression models |
| 5.3 Using vectors and multivariate priors in normal regression models |
| 5.4 Analysis of variance models |
| Problems |
| 6 Incorporating categorical variables in normal models&further modeling issues |
| 6.1 Dummy variables and design matrices |
| 6.2 Analysis of variance models using dummy variables |
| 6.3 Analysis of covariance models |
| 6.4 A Bioassay example |
| 6.5 Further modeling issues |
| 6.6 Closing remarks |
| Problems |
| 7 Introduction to generalized linear models: Binomial and Poisson data |
| 7.1 Introduction |
| 7.2 Prior distributions |
| 7.3 Posterior inference |
| 7.4 Poisson regression models |
| 7.5 Binomial Regression Models |
| 7.6 Models for contingency tables |
| Problems |
| 8 Generalized linear models: Models for positive continuous data, count data and other GLM based extensions |
| 8.1 Models with non-standard distributions |
| 8.2 Models for positive continuous response variables |
| 8.3 Additional models for count data |
| 8.4 Further GLM based models and extensions |
| Problems |
| 9 Bayesian Hierarchical models |
| 9.1 Introduction |
| 9.2 Some simple examples |
| 9.3 The generalized linear mixed model formulation |
| 9.4 Discussion, closing remarks and further reading |
| Problems |
| 10 The predictive distribution and model checking |
| 10.1 Introduction |
| 10.2 Estimating the predictive distribution for future or missing observations using MCMC |
| 10.3 Using the predictive distribution for model checking |
| 10.4 Using cross-validation predictive densities for model checking, evaluation and comparison |
| 10.5 Illustration of a complete predictive analysis: Normal regression models |
| 10.6 Discussion |
| Problems |
| 11 Bayesian Model and Variable Evaluation |
| 11.1 Prior predictive distributions as measures of model comparison: Posterior model odds and Bayes Factors |
| 11.2 Sensitivity of the posterior model probabilities: The Bartlett-Lindley paradox |
| 11.3 Computation of the marginal likelihood |
| 11.4 Computation of the marginal likelihood using WinBUGS |
| 11.5 Bayesian variable selec |
