Bayesian Data Analysis Av Andrew (Columbia University New York New York Usa) Gelman, John B. (Royal Childrens Hospital Parkville Victoria Australia) C

<P><STRONG>Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis</STRONG></P><P>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. <STRONG>Bayesian Data Analysis, Third Edition</STRONG> 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.</P><P><STRONG>New to the Third Edition</STRONG></P><UL><P><LI>Four new chapters on nonparametric modeling</LI><LI>Coverage of weakly informative priors and boundary-avoiding priors</LI><LI>Updated discussion of cross-validation and predictive information criteri

Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow 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 EditionFour new chapters on nonparametric modelingCoverage of weakly informative priors and boundary-avoiding priorsUpdated discussion of cross-validation and predictive information criteriaImproved convergence monitoring and effective sample size calculations for iterative simulationPresentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagationNew and revised software codeThe 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.