Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation

ISBN-10: 0387715983
ISBN-13: 9780387715988
Edition: 2nd 2007
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Description: Winner of the 2004 DeGroot Prize The DeGroot Prize is awarded every two years by the International Society for Bayesian Analysis in recognition of an important, timely, thorough and notably original contribution to the statistics literature.This  More...

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Book details

List price: $59.95
Edition: 2nd
Copyright year: 2007
Publisher: Springer
Publication date: 8/27/2007
Binding: Paperback
Pages: 606
Size: 6.25" wide x 9.00" long x 1.00" tall
Weight: 1.914

Winner of the 2004 DeGroot Prize The DeGroot Prize is awarded every two years by the International Society for Bayesian Analysis in recognition of an important, timely, thorough and notably 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.

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

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