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This paperback edition, a reprint of the 2001 edition, is a graduate-level textbook that introduces Bayesian statistics and decision theory. It covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, Bayesian model choice, hierarchical and empirical Bayes modeling, Monte Carlo integration including Gibbs sampling, and other MCMC techniques. It was awarded the 2004 DeGroot Prize by the International Society for Bayesian Analysis (ISBA) for setting "a new standard for modern textbooks dealing with Bayesian methods, especially those using MCMC techniques, and that it is a worthy successor to DeGroot's and Berger's earlier texts".
Review of the second edition:
"The text reads fluently and beautifully throughout, with light, good-humoured touches that warm the reader without being intrusive. There are many examples and exercises, some of which draw out the essence of work of other authors. Only a few displays and equations have numbers attached. This is an extremely fine, exceptional text of the highest quality." ISI Short Book Reviews

The Bayesian Choice: A Decision-Theoretic Foundation to Computational Implementation

Decision-Theoretic Foundations.- From Prior Information to Prior Distributions.- Bayesian Point Estimation.- Tests and Confidence Regions.- Bayesian Calculations.- Model Choice.- Admissibility and Complete Classes.- Invariance, Haar Measures, and Equivariant Estimators.- Hierarchical and Empirical Bayes Extensions.- A Defense of the Bayesian Choice.