Bayesian data analysis / Andrew Gelman ... [and others].

Includes bibliographical references (pages 611-646) and indexes.

Pt. I. Fundamentals of Bayesian Inference -- 1. Background -- 2. Single-parameter models -- 3. Introduction to multiparameter models -- 4. Large-sample inference and frequency properties of Bayesian inference -- Pt. II. Fundamentals of Bayesian Data Analysis -- 5. Hierarchical models -- 6. Model checking and improvement -- 7. Modeling accounting for data collection -- 8. Connections and challenges -- 9. General advice -- Pt. III. Advanced Computation -- 10. Overview of computation -- 11. Posterior simulation -- 12. Approximations based on posterior modes -- 13. Special topics in computation -- Pt. IV. Regression Models -- 14. Introduction to regression models -- 15. Hierarchical linear models -- 16. Generalized linear models -- 17. Models for robust inference -- 18. Mixture models -- 19. Multivariate models -- 20. Nonlinear models -- 21. Models for missing data -- 22. Decision analysis -- A. Standard probability distributions -- B. Outline of proofs of asymptotic theorems -- C. Example of computation in R and Bugs.