Bayesian Statistics: An Introduction
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.
More and more students are realizing that they need to learn Bayesian statistics to meet their academic and professional goals. This book is best suited for use as a main text in courses on Bayesian statistics for third and fourth year undergraduates and postgraduate students.
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Bayesian inference for the normal distribution
Hypothesis testing 4 1 Hypothesis testing 4 2 Onesided hypothesis tests 4 3 Lindleys method 4 4 Point or sharpnull hypotheses with prior information
3Regression andthebivariate normalmodel 6 4Conjugate
Othertopics 7 1 The likelihoodprinciple 7 2 The stopping rule principle
2TheEM algorithm 9 3Data augmentation by Monte Carlo 9 4TheGibbs sampler
Some approximate methods
Common statistical distributions