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. Suitable for both students and researchers, the book introduces Bayesian inference starting from first principles, presents effective current approaches to Bayesian modeling and computation in statistics and related fields, and provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, are available on the book's web page.