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Bayesian Methods for Data Analysis, Third Editionby Bradley P. Carlin, Thomas A. Louis

June 30, 2008 by Chapman and Hall/CRC
Textbook - 552 Pages - 98 B/W Illustrations
ISBN 9781584886976 - CAT# C6978
Series: Chapman & Hall/CRC Texts in Statistical Science

Features
Offers a state-of-the-art, reader-friendly introduction to hierarchical statistical modeling Provides an up-to-date review of Bayesian computing, including modern MCMC methods and the software used to implement them in Bayesian data analysis Includes practical advice and models for submitting Bayesian drug or medical device applications to the FDA Contains many examples, exercises, selected solutions, and case studies as well as R and WinBUGS code A solutions manual for qualifying instructors contains solutions, computer code, and associated output for every homework problem—available both electronically and in print

Summary
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 modeling—now 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 problem—available 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.