Call number:
PIK M 311-10-0111
Description / Table of Contents:
Contents: Part 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 ; Part 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 ; Part III: Advanced Computation ; 10 Overview of computation ; 11. Posterior simulation ; 12. Approximations based on posterior modes ; 13. Special topics in computation ; Part IV: Regression Models ; 14 Introduction to regression models ; 15. Hierarchical linear models ; 16. Generalized linear models ; 17. Models for robust inference ; Part V: Specific Models and Problems ; 18 Mixture models ; 19. Multivariate models ; 20. Nonlinear models ; 21. Models for missing data ; 22. Decision analysis ; Appendixes ; A Standard probability distributions ; B. Outline of proofs of asymptotic theorems ; C. Example of computation in R and Bugs
Type of Medium:
Monograph available for loan
Pages:
XXV, 668 S. : graph. Darst.
Edition:
2. ed.
ISBN:
158488388X
Series Statement:
Texts in statistical science
Location:
A 18 - must be ordered
Branch Library:
PIK Library
