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Rating: 
Summary: second edition of excellent treatise on Bayesian methods
Review: Robert is the author or co-author of a number of excellently written statistical texts from a Bayesian viewpoint. This text is no exception. It was quite popular in its first edition in 1994 (a translation and correction of an earlier text in French). The rapid advancement in Bayesian applications and theory due to the success of computer-intensive methods such as Markov Chain Monte Carlo Methods justifies an update in 2001.Chapter 7 on model choice is entirely new and Chapter 6 on Bayesian calculations is extensively revised. Chapter 10 on hierarchical models and empirical Bayes extensions has been supplemented with a number of recent examples. Bayesian hierarchical models are now being used in the development of clinical trials particularly in the medical device industry. This is an advanced graduate text in Bayesian statistics and has a wealth of references to the literature. In that respect it is very similar to the fine text by Bernardo and Smith (1994) "Bayesian Theory" but is a little more current. An important reference for all statistical researchers, I highly recommend it for a graduate course text in Bayesian methods as well as for a reference book.
Rating:
Summary: Why you should be bayesian
Review: Robert's defend the bayesian choice is one of the clearest and convincing in the last years. The book is concise and direct and the math kept at an appropriate level. I can only hope Springer republises it ASAP.
Rating: 
Summary: A thorough description of bayesian statistics
Review: The book is a good introduction to bayesian decision theory. The plenty examples in the book are helpful in the understanding of the subject, but one could wish a more detailed description of the bayesian paradigm. People with little experience with statistics should maybe consider another book.
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