Bayesian Methods for Data Analysis: 3rd Edition (Hardback) book cover

Bayesian Methods for Data Analysis

3rd Edition

By Bradley P. Carlin, Thomas A. Louis

Chapman and Hall/CRC

552 pages | 98 B/W Illus.

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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.


    … would appeal to a practising statistician in the pharmaceutical industry. … [Examples] are clearly worked through from start to finish with hints on presentation of results. … I would recommend this book to somebody who is learning Bayesian methods and it would also be useful for those with more experience. … It should sit alongside other good Bayesian book in anybody’s collection.

    —Alun Bedding, Pharmaceutical Statistics, 2010

    … this book will provide considerable value-added to one’s library of Bayesian books. … In the third edition, the authors directly integrate WinBUGS and R routines into their presentation of Bayesian methods and provide some new material along the way, in particular, an excellent discussion of Bayesian design. … an excellent addition to the growing body of books on Bayesian analysis and is a must read for serious students of Bayesian statistics.

    Psychometrika, Vol. 75, No. 2, June 2010

    … the third edition has more of a Bayesian flavor with comprehensive coverage of computational Bayesian statistics, including new additions of BUGS and R code throughout the book and reorganization or expansion of several chapters. … I am glad to see that the software code and examples have also been made available on the website so that users can truly enjoy easy access and convenience in reproducing the computations in the book. In summary, I think this is a very worthy edition and I highly recommend it as a textbook, and for people who deal with biostatistics problems regularly as a good introduction into the literature. Libraries which have the second edition are encouraged to buy this edition as well.

    Journal of Applied Statistics, Vol. 37, No. 4, April 2010

    … the book contains some useful advice for practitioners. All the essential topics are covered … Throughout the text one can find good practical advice on various implementation issues, and there is a whole chapter dedicated to case-studies. The chapter on Bayesian design provides very good coverage of some clinical trial design ideas that are receiving a considerable amount of interest in the pharmaceutical industry currently. This book, by two very experienced and knowledgeable Bayesians, is a valuable contribution to the growing literature on the practical application of Bayesian methods. …

    Journal of the Royal Statistical Society, Series A, Vol. 172, October 2009

    … A strength of this book is the numerous detailed examples that accompany the material in the text. … This is a nice text and would be appropriate as a reference or teaching aid for a graduate-level course in applied Bayesian statistics. The emphasis on biomedical applications makes it a valuable resource for research in biostatistics. …

    Statistics in Medicine, 2009

    I like this book a lot. It’s not the book that I would’ve written, and that’s a good thing. Buying Carlin and Louis along with our book will give you two perspectives on applied Bayesian statistics as it is practiced in the 21st century. … I do think their book is a great complement to ours, with a slightly different perspective, strong coverage of the theoretical issues of point and interval estimation, and a bunch of compelling biomedical examples.

    —Andrew Gelman, Columbia University,, 2008

    with this reorganization of chapters in the third edition, I believe that the authors have made their material more accessible to an applied audience, and I would now seriously consider this book for my class.

    —James H. Albert, Bowling Green State University, Journal of the American Statistical Association, June 2009, Vol. 104, No. 486

    Praise for the Previous Editions

    … particularly recommend the book to practicing biometricians who want to explore the potential for using Bayesian methods in their own work.

    Biometrics, Vol. 57, No. 3, September 2001

    … an important and timely addition to applied statistics … the writing is excellent, and the authors are able to present an amazing amount of material cogently in [a] smaller book … the reader reaps the benefits of being in the hands of a true master …

    Journal of American Statistical Association

    The writing is excellent and the worked examples are also excellent for understanding the methods. In summary, I recommend [it] for advanced graduate students and all research workers.

    —Olaf Berke, Computational Statistics & Data Analysis, January 2001

    Table of Contents

    Approaches for statistical inference


    Motivating Vignettes

    Defining the Approaches

    The Bayes-Frequentist Controversy

    Some Basic Bayesian Models

    The Bayes approach


    Prior Distributions

    Bayesian Inference

    Hierarchical Modeling

    Model Assessment

    Nonparametric Methods

    Bayesian computation


    Asymptotic Methods

    Noniterative Monte Carlo Methods

    Markov Chain Monte Carlo Methods

    Model criticism and selection

    Bayesian Modeling

    Bayesian Robustness

    Model Assessment

    Bayes Factors via Marginal Density Estimation

    Bayes Factors via Sampling over the Model Space

    Other Model Selection Methods

    The empirical Bayes approach


    Parametric EB Point Estimation

    Nonparametric EB Point Estimation

    Interval Estimation

    Bayesian Processing and Performance

    Frequentist Performance

    Empirical Bayes Performance

    Bayesian design

    Principles of Design

    Bayesian Clinical Trial Design

    Applications in Drug and Medical Device Trials

    Special methods and models

    Estimating Histograms and Ranks

    Order Restricted Inference

    Longitudinal Data Models

    Continuous and Categorical Time Series

    Survival Analysis and Frailty Models

    Sequential Analysis

    Spatial and Spatio-Temporal Models

    Case studies

    Analysis of Longitudinal AIDS Data

    Robust Analysis of Clinical Trials

    Modeling of Infectious Diseases


    Distributional Catalog

    Decision Theory

    Answers to Selected Exercises


    Author Index

    Subject Index


    Exercises appear at the end of each chapter.

    About the Series

    Chapman & Hall/CRC Texts in Statistical Science

    Learn more…

    Subject Categories

    BISAC Subject Codes/Headings:
    MATHEMATICS / Probability & Statistics / General