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 Title: Bayesian Data Analysis
 Author(s) Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
 Publisher: Chapman and Hall/CRC; 3rd edition (2013); eBook (Errors Fixed Edition, 2021)
 Permission: PDF available for download for noncommercial purposes.
 Hardcover: 675 pages
 eBook: PDF (677 pages)
 Language: English
 ISBN10/ASIN: 1439840954
 ISBN13: 9781439840955
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Book Description
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 uptodate Bayesian methods.
The authors  all leaders in the statistics community  introduce basic concepts from a dataanalytic 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 boundaryavoiding priors
 Updated discussion of crossvalidation 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
 N/A
 Bayesian Thinking
 Data Analysis and Data Mining
 Statistics, Mathematical Statistics
 Probability and Stochastic

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