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Synopsis
A concise account of the ways in which the Bayesian approach to statistics develops and the contrast between it and the conventional approach. Rather than doing everything in the greatest generality to begin with, theory is built up step-by-step. Essential concepts are brought out of discussions of the salient features of specific examples.
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From the Publisher
This new edition of Lee's popular book introduces the Bayesian philosophy of statistics. It has been completely updated and features new chapters on Gibbs sampling and hierarchical methods and more exercises.
From the Back Cover
Bayesian Statistics is the school of thought that uses all information surrounding the likelihood of an event rather than just that collected experimentally. Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter Lee’s well-established introduction maintains the clarity of exposition and use of examples for which this text is known and praised. In addition, there is extended coverage of the Metropolis-Hastings algorithm as well as an introduction to the use of BUGS (Bayesian Inference Using Gibbs Sampling) as this is now the standard computational tool for such numerical work. Other alterations include new material on generalized linear modelling and Bernardo’s theory of reference points.
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Bayesian Statistics: An Introduction
Seller: crealivres, La fontennelle, France
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Condition: Very Good. Envoi rapide Très Bon Etat de conservation couverture un peu défraîchie intérieur propre ex-libris. 15 85x2 06x23 16cm. 1997. Broché. 360 pages. Very Good. Seller Inventory # 100133725
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