An introduction to Bayesian philosophy for statistics students. This edition contains new chapters on Gibbs sampling, computer-intensive methods, and hierarchical methods. There are examples throughout, and the number of exercises has been increased and the whole text brought up-to-date.

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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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