Ioannis Ntzoufras

Bayesian Modeling Using WinBUGS

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Wiley Series in Computational Statistics
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 520
• Erscheinungstermin: 3. Februar 2009
• Englisch
• Abmessung: 240mm x 161mm x 32mm
• Gewicht: 820g
• ISBN-13: 9780470141144
• ISBN-10: 047014114X
• Artikelnr.: 25560041

Autorenporträt

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.

Inhaltsangabe

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 Nonconjugate
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.
WinBUGS Software: Introduction, Setup 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. 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
and Further Modeling Issues. 6.1 Analysis of variance models using dummy
variables. 6.2 Analysis of covariance models. 6.3 A Bioassay example. 6.4
Further modeling issues. 6.5 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 response models. 7.6 Models for contingency tables.
Problems. 8. Models for Positive Continuous Data, Count Data, and Other
GLM-Based Extensions. 8.1 Models with nonstandard 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 Lindley-Bartlett paradox. 11.3 Computation of the
marginal likelihood. 11.4 Computation of the marginal likelihood using
WinBUGS. 11.5 Bayesian variable selection using Gibbs-based methods. 11.6
Posterior inference using the output of Bayesian variable selection
samplers. 11.7 Implementation of Gibbs variable selection in WinBUGS using
an illustrative example. 11.8 The Carlin Chib's method. 11.9 Reversible
jump MCMC (RJMCMC). 11.10 Using posterior predictive densities for model
evaluation. 11.11 Information criteria. 11.12 Discussion and further
reading. Problems. Appendix A: Model Specification via Directed Acyclic
Graphs: The Doodle Menu. A.1 Introduction: Starting with DOODLE. A.2 Nodes.
A.3 Edges. A.4 Panels. A.5 A simple example. Appendix B: The Batch Mode:
Running a Model in the Background Using Scripts. B.1 Introduction. B.2
Basic commands: Compiling and running the model. Appendix C: Checking
Convergence Using CODA/BOA. C.1 Introduction. C.2 A short historical
review. C.3 Diagnostics implemented by CODA/BOA. C.4 A first look of
CODA/BOA. C.5 A simple example. Appendix D: Notation Summary. D.1 MCMC. D.2
Subscripts and indices. D.3 Parameters. D.4 Random variables and data. D.5
Sample estimates. D.6 Special functions, vectors and matrices. D.7
Distributions. D.8 Distribution-related notation. D.9 Notation used in
ANOVA and ANCOVA. D.10 Variable and model specification. D.11 Deviance
information criterion (DIC). D.12 Predictive measures. References. Index.