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Bayesian Data Analysis

5 out of 5 stars 4 customer reviews

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Product Details

  • Paperback: 896 pages
  • Publisher: ROUTLEDGE (July 29, 2003)
  • Language: English
  • ASIN: B000JZ6AKK
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (4 customer reviews)

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Format: Hardcover Verified Purchase
Note, this is a review of the first edition.

Overview

This book was the textbook used at the University of Wisconsin-Madison for the graduate course in Bayesian Decision and Control I during the fall of 2001 and 2002. It strikes a good balance between theory and practical example, making it ideal for a first course in Bayesian theory at an intermediate-advanced graduate level. Its emphasis is on Bayesian modeling and to some degree computation.

Prerequisites

While no Bayesian theory is assumed, it is assumed that the reader has a background in mathematical statistics, probability and continuous multi-variate distributions at a beginning or intermediate graduate level. The mathematics used in the book is basic probability and statistics, elementary calculus and linear algebra.

Intended audience

This book is primarily for graduate students, statisticians and applied researchers who wish to learn Bayesian methods as opposed to the more classical frequentist methods.

Material covered

It covers the fundamentals starting from first principles, single-parameter models, multi-parameter models, large sample inference, hierarchical models, model checking and sensitivity analysis (model checking and sensitivity analysis are especially well covered), study design, regression models, generalized linear models, mixture models and models for missing data. In addition it covers posterior simulation and integration using rejection sampling and importance sampling. There is one chapter on Markov chain Monte Carlo simulation (MCMC) covering the generalized Metropolis algorithm and the Gibbs sampler.

Over 38 models are covered, 33 detailed examples from a wide range of fields (especially biostatistics).
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Format: Hardcover
First, I must admit a bias: I frequently work with one of the authors (Gelman), and I think highly of his work and statistical judgment.
This book's biggest strength is its introduction of most of the important ideas in Bayesian statistics through well-chosen examples. These are examples are not contrived: many of them came up in research by the authors over the past several years. Most examples follow a logical progression that was probably used in the original research: a simple model is fit to data; then areas of model mis-fit are sought, and a revised model is used to address them. This brings up another strength of the book: the discussion and treatment of measures of model fit (and sensitivity of inferences) is lucid and enlightening.
Some readers may wish the computational methods were spelled out more fully: this book will help you choose an appropriate statistical model, and the ways to look for serious violations of it, but it will take a bit of work to convert the ideas into computational algorithms. This is not to say that the computational methods aren't discussed, merely that many of the details are left to the reader. The reader expecting pseudo-code programs will be disappointed.
All in all, I recommend this book for anyone who applies statistical models to data, whether those models are Bayesian or not. I especially recommend it for researchers who are curious about Bayesian methods but do not see the point of them---Chapter 5, and particularly section 5.5 (an example chosen from educational testing), beautifully addresses this issue.
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Format: Hardcover
This is a well written text that is fast becoming a classic reference. It contains a wealth of good applications. It is one of the new books that presents the growing use of Bayesian methods in practice since the advancement of Markov Chain Monte Carlo approach. It includes a whole chapter the Markov chain approach to computation. Other strengths of the book include the chapter on missing data and the chapter that provides expert advice. It is one of the best books ever written on the practical aspects of modern Bayesian analysis. I know one of the authors very well (Hal Stern) and am familiar with the fine research work of the others. Don Rubin brings a wealth of knowledge and experience in statistical methods and Bayesian analysis to the table. He is also the inventor of the Bayesian bootstrap.

Another text in the CRC series Markov Chain Monte Carlo in Practice by Gilks, Richardson and Spiegelhalter provides more detail on these methods along with many applications including some Bayesian ones.
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Format: Hardcover Verified Purchase
This book may seem a bit dated. But the descriptions and writing are so good it definitely in that timeless category. Recommended for graduate students in the sciences. I had the pleasure of listening to Gelman give a talk and his witty personality comes through in his text as well.
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