Bayesian Statistics: an Introduction

✍ Scribed by Lee, Peter

• Publisher: John Wiley & Sons
• Year: 2012
• Tongue: English
• Leaves: 550
• Edition: 4
• Category: Library

✦ Synopsis

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee's book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notions such as sufficiency are brought out of a discussion of the salient features of specific examples.

✦ Table of Contents

Bayesian Statistics
Title Page
Contents
Preface
Preface to the First Edition
1 Preliminaries
2 Bayesian inference for the normal distribution
3 Some other common distributions
4 Hypothesis testing
5 Two-sample problems
6 Correlation, regression and the analysis of variance
7 Other topics
8 Hierarchical models
9 The Gibbs sampler and other numerical methods
10 Some approximate methods
Appendix A Common statistical distributions
Appendix B Tables
Appendix C R programs
Appendix D Further reading
References
Index
Erratas
Answers to Exercises
Further Exercises