Bayesian Data Analysis, Third Edition

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

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November 1, 2013 by Chapman and Hall/CRC
Textbook - 675 Pages - 121 B/W Illustrations
ISBN 9781439840955 - CAT# K11900
Series: Chapman & Hall/CRC Texts in Statistical Science

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Features

  • Presents an accessible introduction to Bayesian statistics
  • Focuses on the use of Bayesian inference in practice, with many examples of real statistical analyses throughout
  • Includes plenty of exercises and bibliographic notes at the end of each chapter
  • Provides data sets, solutions to selected exercises, and other material online

Summary

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.