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 analysis from a Bayesian perspective. Changes in this edition include: additional material on how Bayesian methods are connected to other approaches, stronger focus on MCMC, a chapter on advanced computation topics, more examples, and additional chapters on current models for Bayesian data analysis, such as equation models and generalized linear mixed models. This is both an introductory textbook and a reference working scientists will use throughout their professional life

Product Details

• Amazon Sales Rank: #40369 in Books
• Published on: 2003-07-29
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 696 pages

From the Back Cover
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:

Customer Reviews

The Standard Work
In my view, this is the single best book on Bayesian statistics. It's set at about masters level for a statistics specialist, though it could be read by anyone with matrix algebra and calculus. It starts right from scratch, with basic ideas about probability and develops Bayesian ideas through simple one-parameter models right up to the most sophisticated types of heirarchical models extant. Because the subject matter was formerly the subject of heated debate at the philosophical level this book carefully avoids philosophical argument. The authors prefer to make their case by presenting the reader with
a wide range of powerful techniques and leaving the philosophy to others. Each chapter ends with a guide to the literature on the subject matter of the chapter. The tone of the book is practical and gives much guidance on computational issues. The second edition made a great book even better by beefing up the parts on computation to include more on how to implement state-of-the-art Markov Chain Monte Carlo methods using freely available software (R and WinBUGS) as well as how to write your own. Outstanding!

Bayesian Data Analysis
This is an excellent book. Philosophical ramblings are more or less avoided, and the authors get down to analysing data. Chapters 3 and 5 take the reader through basic Bayesian analysis including posterior simulation and hierarchial modelling. There's quite a bit of Greek stuff and assumptions of conjugacy, but the approach is still very practical. A worked example of a hierarchical model is given in sufficient detail for the reader to reproduce.

The chapter that introduces MCMC is extremely good and also very practical. From studying this chapter, I was able to go from knowing nothing about MCMC to programming my first Metropolis algorithm. Going back through the chapter a couple of times, I was then able to program a Metropolis algorithm for a novel application, and to build in tuning steps and assess convergence. All from one chapter.

The later part of the book has a vaguer feel to it. Many of the models are described in quite high level terms and details of computations are less forthcoming. It does, however, give a very strong impression of just how diverse the applications of MCMC are.

If you want to learn Bayesian data analysis, this book is the one you're looking for.