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.
1. Introduction to Bayesian inference
2. Markov Chain Monte Carlo Algorithms in Bayesian Inference
3. WinBUGS Software: Introduction, Setup and Basic Analysis
4. WinBUGS Software: Illustration, Results, and Further Analysis
5. Introduction to Bayesian Models: Normal models
6. Incorporating Categorical Variables in Normal Models and Further Modeling Issues
7. Introduction to Generalized Linear Models: Binomial and Poisson Data
8. Models for Positive Continuous Data, Count Data, and Other GLM-Based Extensions
9. Bayesian Hierarchical Models
10. The Predictive Distribution and Model Checking
11. Bayesian Model and Variable Evaluation
Appendix A: Model Specification via Directed Acyclic Graphs: The Doodle Menu
Appendix B: The Batch Mode: Running a Model in the Background Using Scripts
Appendix C: Checking Convergence Using CODA/BOA
Appendix D: Notation Summary
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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.