Table of Contents

FUNDAMENTALS OF BAYESIAN INFERENCE
Background
Single-Parameter Models
Introduction to Multiparameter Models
Large-Sample Inference and Connections to Standard Statistical Methods

FUNDAMENTALS OF BAYESIAN DATA ANALYSIS
Hierarchical Models
Model Checking and Improvement
Modeling Accounting for Data Collection
Connections and Controversies
General Advice

ADVANCED COMPUTATION
Overview of Computation
Posterior Simulation
Approximations Based on Posterior Modes
Topics in Computation

REGRESSION MODELS
Introduction to Regression Models
Hierarchical Linear Models
Generalized Linear Models
Models for Robust Inference and Sensitivity Analysis
Analysis of Variance

SPECIFIC MODELS AND PROBLEMS
Mixture Models
Multivariate Models
Nonlinear Models
Models for Missing Data
Decision Analysis

APPENDICES
A: Standard Probability Distributions
B: Outline of Proofs of Asymptotic Theorems
C: Example of Computation in R and Bugs
References

Reviews

"If you have done some Bayesian modeling, using WinBUGS, and are anxious to take the next steps to more sophisticated modeling and diagnostics, then the book offers a wealth of advice This is a book that challenges the user in its sophisticated approach toward data analysis in general and Bayesian methods in particular. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems." -John Grego, University of South Carolina "Bayesian Data Analysis is easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods" -Prof. David Blackwell, Department of Statistics, University of California, Berkeley Praise for the first edition: "A tour de force... it is far more than an introductory text, and could act as a companion for a working scientist from undergraduate level through to professional life." -Robert Matthews, Aston University, in New Scientist "an essential reference text for any applied statistician" -Stephen Brooks, University of Cambridge, in The Statistician "will contribute to closing the gap between scientists and statisticians" -Sander Greenland, UCLA, in American Journal of Epidemiology "an excellent teaching reference for advanced undergraduate and graduate courses" -Nicky Best, Imperial College School of Medicine, in Statistics in Medicine