Thoroughly updated and expanded, this successful introduction to the subject includes a new chapter on hierarchical methods in Bayesian statistics and gives a fuller treatment of empirical Bayes methods. It also includes a chapter on real numerical methods, especially the EM algorithm and Gibbs sampling, and a description of Bayes linear methods.
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This new edition of Lee's popular book introduces the Bayesian philosophy of statistics. It has been completely updated and features new chapters on Gibbs sampling and hierarchical methods and more exercises.From the Back Cover:
Bayesian Statistics is the school of thought that uses all information surrounding the likelihood of an event rather than just that collected experimentally. Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter Lee’s well-established introduction maintains the clarity of exposition and use of examples for which this text is known and praised. In addition, there is extended coverage of the Metropolis-Hastings algorithm as well as an introduction to the use of BUGS (Bayesian Inference Using Gibbs Sampling) as this is now the standard computational tool for such numerical work. Other alterations include new material on generalized linear modelling and Bernardo’s theory of reference points.
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