Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)

Gelman, Andrew

ISBN 10: 1439840954 / ISBN 13: 9781439840955

Published by Chapman and Hall/CRC, 2013

About this Item

Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

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Bibliographic Details

• Title: Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2013
• Language: English
• ISBN 10: 1439840954
• ISBN 13: 9781439840955
• Binding: hardcover
• Edition: 3rd Edition.
• Condition: Good

About this title

Synopsis

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors-all leaders in the statistics community-introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.

New to the Third Edition

• Four new chapters on nonparametric modeling
• Coverage of weakly informative priors and boundary-avoiding priors
• Updated discussion of cross-validation and predictive information criteria
• Improved convergence monitoring and effective sample size calculations for iterative simulation
• Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation
• New and revised software code

The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book's web page.

About the Author

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Doanld B. Rubin