Bayesian data analysis.
2nd ed.

*(English)*Zbl 1039.62018
Boca Raton, FL: Chapman and Hall/CRC (ISBN 1-58488-388-X/hbk). xxv, 668 p. (2004).

This text is accessible to readers with some background in probability and statistics and some knowledge of linear algebra and calculus. It could be used both as a course text for a graduate level course and as a handbook for those contemplating Bayesian analysis. The software R and BUGS are used. Many real data sets illustrate the methods. A comprehensive review of current Bayesian inference is presented, one of the changes to the 1st edition, which appeared in 1995 and has been reprinted in 1998, see the review Zbl 0914.62018, is a modification of the section on computation which reflects advances in MCMC methods. The book is organised into 5 parts with 22 chapters as follows:

Part I: Fundamentals of Bayesian Inference (including single and multiparameter). Part II: Fundamentals of Bayesian Data Analysis (including model checking). Part III: Advanced Computation. Part IV: Regression Models (including HLM and GLM).

Part V: Other Bayesian Models (mixtures, multivariate, nonlinear, missing data and decision analysis).

Each chapter includes exercises and bibliographic notes.

Part I: Fundamentals of Bayesian Inference (including single and multiparameter). Part II: Fundamentals of Bayesian Data Analysis (including model checking). Part III: Advanced Computation. Part IV: Regression Models (including HLM and GLM).

Part V: Other Bayesian Models (mixtures, multivariate, nonlinear, missing data and decision analysis).

Each chapter includes exercises and bibliographic notes.

Reviewer: Peter Watts Jones (Keele)