Presents an introduction to the foundations and applications of Bayesian analysis. This book focuses on hierarchical Bayesian modeling as implemented through Markov chain Monte Carlo (MCMC) methods and related data analytic techniques.
Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques.
New to the Third Edition
New data examples, corresponding R and WinBUGS code, and homework problems
Explicit descriptions and illustrations of hierarchical modelingnow commonplace in Bayesian data analysis
A new chapter on Bayesian design that emphasizes Bayesian clinical trials
A completely revised and expanded section on ranking and histogram estimation
A new case study on infectious disease modeling and the 1918 flu epidemic
A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problemavailable both electronically and in print
Ideal for Anyone Performing Statistical Analyses
Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.
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