The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation
**Winner of the 2004 DeGroot Prize**The DeGroot Prize is awarded every two years by the International Societyfor Bayesian Analysis in recognition of an important, timely, thorough andnotably original contribution to the statistics literature.This graduate-level textbook presents an introduction to Bayesian statisticsand decision theory. Its scope covers both the basic ideas of statisticaltheory, and also some of the more modern and advanced topics of Bayesianstatistics such as complete class theorems, the Stein effect, Bayesian modelchoice, hierarchical and empirical Bayes modeling, Monte Carlo integration,including Gibbs sampling and other MCMC techniques.The second edition includes a new chapter on model choice (Chapter 7) and thechapter on Bayesian calculations (6) has been extensively revised. Chapter 4includes a new section on dynamic models. In Chapter 3, the material onnoninformative priors has been expanded, and Chapter 10 has been supplementedwith more examples. The Bayesian Choice will be suitable as a text for courseson Bayesian analysis, decision theory or a combination of them.