Bayesian data analysis / Andrew Gelman, John Carlin, Hal S. Stern and Donald B. Rubin.
Series Chapman & Hall texts in statistical science seriesEditor: London : Chapman & Hall, 1995Descripción: xix, 526 p. ; 24 cmISBN: 0412039915Otra clasificación: 62F15Part 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 methodsPart 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 modelsPart III. Advanced computation: 9. Approximations based on posterior modes; 10. Posterior simulation and integration; 11. Markov chain simulationPart 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.
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| 62 F951 Multivariate quality control : | 62 G163 Nonlinear statistical models / | 62 G319 Bayesian data analysis / | 62 G319 Bayesian data analysis / | 62 G427 Sequential tests of statistical hypotheses / | 62 G441 Nonparametric statistical inference / | 62 G458 Nonlinear multivariate analysis / |
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.
MR, 97c:62059
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