The Bayesian Choice: From Decision-theoretic Foundations To Computational Implementation by Christian Robert

The Bayesian Choice: From Decision-theoretic Foundations To Computational Implementation

byChristian Robert

Paperback | August 27, 2007

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WINNER OF THE 2003 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 statistics and decision theory. Its scope 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. The second edition includes a new chapter on model choice (Chapter 7) and the chapter on Bayesian calculations (6) has been extensively revised. Chapter 4 includes a new section on dynamic models. In Chapter 3, the material on noninformative priors has been expanded, and Chapter 10 has been supplemented with more examples. The Bayesian Choice will be suitable as a text for courses on Bayesian analysis, decision theory or a combination of them.

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Title:The Bayesian Choice: From Decision-theoretic Foundations To Computational ImplementationFormat:PaperbackDimensions:628 pages, 9.25 × 6.1 × 0.03 inPublished:August 27, 2007Publisher:Springer New YorkLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0387715983

ISBN - 13:9780387715988

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Table of Contents

Decision-theoretic foundations of statistical inference.- 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.

Editorial Reviews

From the reviews of the second edition:SHORT BOOK REVIEWS"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. Each chapter ends with a "Notes" section containing further brief descriptions of research papers. A reference section lists about eight hundred and sixty references. Each chapter begins with a quotation from "The Wheel of Time" a sequence of books by Robert Jordan. Only a few displays and equations have numbers attached. This is an extremely fine, exceptional text of the highest quality."ISI Short Book Reviews, April 2002JOURNAL OF MATHEMATICAL PSYCHOLOGY"This book is an excellent introduction to Bayesian statistics and decision making. The author does an outstanding job in explicating the Bayesian research program and in discussing how Bayesian statistics differs form fiducial inference and from the Newman-Pearson likelihood approach.The book would be well suited for a graduate-level course in a mathematical statistics department. There are numerous examples and exercises to enhance a deeper understanding of the material. The writing is authoritative, comprehensive, and scholarly.""This book is a publication in the well-known Springer Series in statistics published in 2001. It is a textbook that presents an introduction to Bayesian statistics and decision theory for graduate level course . . The textbook contains a wealth of references to the literature; therefore it can also be recommended as an important reference book for statistical researchers. . for those who want to make a Bayesian choice, I recommend that you make your choice by getting hold of Robert's book, The Bayesian Choice." (Jan du Plessis, Newsletter of the South African Statistical Association, June, 2003)"This is the second edition of the author's graduate level textbook 'The Bayesian choice: a decision-theoretic motivation.' . The present book is a revised edition. It includes important advances that have taken place since then. Different from the previous edition is the decreased emphasis on decision-theoretic principles. Nevertheless, the connection between Bayesian Statistics and Decision Theory is developed. Moreover, the author emphasizes the increasing importance of computational techniques." (Krzysztof Piasecki, Zentralblatt MATH, Vol. 980, 2002)