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Rating: 
Summary: Still one of the best
Review: After about 30 years this book is still considered a classic worthy reading in details. Neatly called "Box & Tiao" in this reprint edition, it shows how this book came to be widely known in the tight Bayesian circle, which now grows at an exponential rate. I was looking for a Bayesian book which can tell you how you can do the Bayesian approach, NOT why you should do it. (The latter was needed before, but has become irrelevant in practice.) This book started with a good promise of working out a number of standard statistical problems using the Bayesian approach, it concluded with a marvelous job of accomplishing that huge task with details and accuracy. I generally agree with Stateman13's review in that this book really elaborates Jeffreys noninformative Bayesian approach, which I think should be the preferred approach. However, I consider this book one of the best Bayesian books avilable, even better than some of the much newer books. (Tendency to rush publication will not get it done.) This book is a labor of pains but is an artwork worthy your precious bucks!
Rating: 
Summary: Bayesian Inference in Statistical Analysis
Review: Have you ever wondered about the origins and meaning of statistical concepts? Most of the books on Statistics shy away from this topic, they just throw formulae at you! Not this book. In the first two chapters it goes to great extent to explain a very important concept of noninformative prior. It also states very clearly the differencies between more traditional Sampling Theory approach and Bayesian Analysis. While majority of Statisticians prefer the ideas and notions of Sampling Theory, majority of Scientists and Control System Engeneers are more inclined to use Bayesian Analysis because of its practicality. This book gives a plenty of material on Bayesian Inference and shows how to put theoretical knowledge into practice. It presents the material in transparent and orderly fashion but it requires certain degree of mathematical sophistication. A prerequisite would be any standard text book on Statistical Inference.
Rating:
Summary: classic text on Bayesian methods
Review: This is a book written in 1973 but showing the brilliance and forethought of George Box. Wiley reprinted it in its popular paperback classic series in 1992. The first few chapters introduce Bayesian ideas and show how with noninformative priors the Bayesian results resemble the classical frequentist results. This essentially reviews the work pioneered by Harold Jeffreys. In the latter chapters more complex problems are introduced including many that do not have nice classical solutions. Box and Tiao show how Bayesian methods contribute ideas that provide new insights into these problems. The discussion of hierarchical models anticipated the developments in Bayesian methods that occurred in the 1990 when the MCMC methods burst onto the scene. This book is nice for a historical perspective but anyone seriously interested in doing modern Bayesian analysis needs a book that deals with the MCMC methods and there are many nice books available these days.
Rating:
Summary: classic text on Bayesian methods
Review: This is a book written in 1973 but showing the brilliance and forethought of George Box. Wiley reprinted it in its popular paperback classic series in 1992. The first few chapters introduce Bayesian ideas and show how with noninformative priors the Bayesian results resemble the classical frequentist results. This essentially reviews the work pioneered by Harold Jeffreys. In the latter chapters more complex problems are introduced including many that do not have nice classical solutions. Box and Tiao show how Bayesian methods contribute ideas that provide new insights into these problems. The discussion of hierarchical models anticipated the developments in Bayesian methods that occurred in the 1990 when the MCMC methods burst onto the scene. This book is nice for a historical perspective but anyone seriously interested in doing modern Bayesian analysis needs a book that deals with the MCMC methods and there are many nice books available these days.
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