Bayesian Data Analysis: Texts in Statistical Science
Gelman, Andrew; John B. Carlin; Hal S. Stern; David B. Dunson; Aki Vehtari; Donald B. Rubin
Language: English
Published by CRC Press | Taylor & Francis Group, Boca Raton, FL, 2015
Series: Book 29 of 126 - Chapman & Hall/CRC Texts in Statistical Science
- Hardcover
- Used
Seller: Twice Sold Tales, Capitol Hill, Seattle, WA, U.S.A.Twice Sold Tales, Capitol Hill
AbeBooks seller since August 31, 1999
Condition: Used - Very good
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Third Edition. Minor rubbing, bumping and scuffing to boards. Pages are clean and appear to be free of writing and highlighting. Binding is tight.
Seller Inventory # 11264
- Title
- Bayesian Data Analysis: Texts in Statistical Science
- Author
- Gelman, Andrew; John B. Carlin; Hal S. Stern; David B. Dunson; Aki Vehtari; Donald B. Rubin
- Publisher
- CRC Press | Taylor & Francis Group, Boca Raton, FL
- Publication year
- 2015
- Condition
- Very Good
- Binding
- Hardcover, 667 pages
- Language
- English
- ISBN 10
- 1439840954
- ISBN 13
- 9781439840955
- Edition
- Third Edition.
- Series
- Book 29 of 126: Chapman & Hall/CRC Texts in Statistical Science
- Seller catalogs
- Science
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.
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
Twice Sold Tales, Capitol Hill
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