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Bayesian Data Analysis
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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 non-commercial purposes.
  • Hardcover: 675 pages
  • eBook: PDF (677 pages)
  • Language: English
  • ISBN-10/ASIN: 1439840954
  • ISBN-13: 978-1439840955
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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 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
About the Authors
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