Bayesian data analysis
Andrew Gelman, John Carlin, Hal S. Stern and Donald B. Rubin.
London : Chapman & Hall, 1995.
xix, 526 págs. ; 24 cm.
Serie: Chapman & Hall texts in statistical science series
ISBN: 0412039915
Reseña: MathSciNet, 97c:62059
Contenido
- Part I. Fundamentals of Bayesian inference: 1. Background; 2. Single-parameter models; 3. Introduction to multiparameter models; 4. Large-sample inference and connections to standard statistical methods
- Part II. Fundamentals of Bayesian data analysis: 5. Hierarchical models; 6. Model checking and sensitivity analysis; 7. Study design in Bayesian analysis; 8. Introduction to regression models
- Part III. Advanced computation: 9. Approximations based on posterior modes; 10. Posterior simulation and integration; 11. Markov chain simulation
- Part IV. Specific models: 12. Models for robust inference and sensitivity analysis; 13. Hierarchical linear models; 14. Generalized linear models; 15. Multivariate models; 16. Mixture models; 17. Models for missing data; 18. Concluding advice.
