Product Description

Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: ·Stronger focus on MCMC·Revision of the computational advice in Part III·New chapters on nonlinear models and decision analysis·Several additional applied examples from the authors' recent research·Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more·Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

Product Details

• Amazon Sales Rank: #21414 in Books
• Published on: 2003-07-29
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 696 pages

Review
Bayesian Data Analysis is easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods
- David Blackwell, Department of Statistics, University of California, Berkeley, USA

Bayesian Data Analysis is easily the most comprehensive, scholarly, and thoughtful book on the subject, and I think will do much to promote the use of Bayesian methods
-Prof. David Blackwell, Department of Statistics, University of California, Berkeley

If you have done some Bayesian modeling, using WinBUGS, and are anxious to take the next steps to more sophisticated modeling and diagnostics, then the book offers a wealth of advice… This is a book that challenges the user in its sophisticated approach toward data analysis in general and Bayesian methods in particular. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems.
-John Grego, University of South Carolina

If you have done some Bayesian modeling, using WinBUGS, and are anxious to take the next steps to more sophisticated modeling and diagnostics, then the book offers a wealth of advice… This is a book that challenges the user in its sophisticated approach toward data analysis in general and Bayesian methods in particular. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems.
-John Grego, University of South Carolina, USA

Praise for the first edition:
A tour de force... it is far more than an introductory text, and could act as a companion for a working scientist from undergraduate level through to professional life.
-Robert Matthews (Aston University), New Scientist

Praise for the first edition:
A tour de force... it is far more than an introductory text, and could act as a companion for a working scientist from undergraduate level through to professional life.
-Robert Matthews, Aston University, in New Scientist

an essential reference text for any applied statistician
-Stephen Brooks (University of Cambridge), The Statistician

an essential reference text for any applied statistician
-Stephen Brooks, University of Cambridge, in The Statistician

an excellent teaching reference for advanced undergraduate and graduate courses
-Nicky Best, (Imperial College School of Medicine), Statistics in Medicine

an excellent teaching reference for advanced undergraduate and graduate courses
-Nicky Best, Imperial College School of Medicine, in Statistics in Medicine

will contribute to closing the gap between scientists and statisticians
-Sander Greenland (University of California, Los Angeles), American Journal of Epidemiology

will contribute to closing the gap between scientists and statisticians
-Sander Greenland, UCLA, in American Journal of Epidemiology

…it is simply the best all-around modern book focused on data analysis currently available. … There is enough important additional material here that those with the first edition should seriously consider updating to the new version. … when students or colleagues ask me which book they need to start with in order to take them as far as possible down the road toward analyzing their own data, Gelman et al. has been my answer since 1995. The second edition makes this an even more robust choice.
-Lawrence Joseph (Montreal General Hospital and McGill University, Canada) Statistics in Medicine, Vol. 23, 2004

Customer Reviews

Likely the best survey book on applied Bayesian theory
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). Each of the 18 chapter has a bibliographic note at the end. There are two appendixes: A) a very helpful list of standard probability distributions and B) outline of proofs of asymptotic theorems.

Sixteen of the 18 chapters end with a set of exercises that range from easy to quite difficult. Most of the students in my fall 2001 class used the statistical language R to do the exercises.

The book's emphasis is on applied Bayesian analysis. There are no heavy advanced proofs in the book. While the proofs of the basic algorithms are covered there are no algorithms written in pseudo code...Additional books of related interest

1) Statistical Decision Theory and Bayesian Analysis, James Berger, second edition. Emphasis on decision theory and more difficult to follow than Gelman's book. Covers empirical and hierarchical Bayes analysis. More philosophical challenging than Gelman's book.

2) Monte Carlo Statistical Methods, Robert and Casella. Very mathematically oriented book. Does a good job of covering MCMC.

3) Monte Carlo Methods in Bayesian Computation, Ming-Hui Chen, Qi-Man Shao, Joseph George Ibrahim. An enormous number of algorithms related to MCMC not covered elsewhere. If you need MCMC and need an algorithm to implement MCMC this is the book to read.

4) Monte Carlo Strategies in Scientific Computing, Jun S. Liu. Covers a wide range of scientific disciplines and how Monte Carlo methods can be used to solve real world problems. Includes hot topics such as bioinformatics. Very concise. Well written, but requires effort to understand as so many different topics are covered. This book is my most often borrowed book on Monte Carlo methods. Jun S. Liu is a big gun at Harvard.

5) Probabilistic Networks and Expert Systems. Cowell, Dawid, Lauritzen, Spiegelhalter. Covers the theory and methodology of building Bayesian networks (probabilistic networks).

Review by a user of the book and colleague of an author
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.

A good introductory book, but...
I read the other reviews and agree with them to some extent. This is
a good introduction to applied Bayesian analysis. Lots of
good examples, illustrations and exercises.

If you are the kind of person who learns by way of examples, then
this might be the text book for you. If you are looking for the
bigger picture, then you will be lost here. There is very little in the way
of theory. Why is this the right method? What is gained theoretically
over a frequentist method? What are the theoretical properties of the
proposed approach? To a large extent these kinds of questions remain a mystery.
In terms of flexibility an applied Bayesian approach has some decided
advantages. However, in terms of theory
it's almost as if the authors want you to believe that once
you adopt the Bayesian approach then the benefits of averaging
by way of using a prior will always be the right thing to do.

You could argue that advanced questions like this are better suited for
a more advanced text book. I tend to ask more out of a book.