Bayesian Methods: A Social and Behavioral Sciences Approach, Second Edition (Statistics in the Social and Behavioral...

Features

• Cover Type: Hard Cover with 752 pages
• Published by: Chapman & Hall/CRC
• Edition: 2nd Edition November 26, 2007
• Written in: English
• ISBN 10 Number: 1584885629
• ISBN 13 Number: 978-1584885627
• Book Dimensions: 9.3 x 6.2 x 1.7 inches
• Weighs: 2.5 pounds

Product Review
Praise for the First Edition:
This book is a brilliant and importantly very accessible introduction to the concept and application of Bayesian approaches to data analysis. The clear strength of the book is in making the concept practical and accessible, without necessarily dumbing it down.  The coverage is also remarkable.
-Dr. S.V. Subramanian, Harvard School of Public Health, Cambridge, Massachusetts, USA

One of the signal contributions of Bayesian Methods: A Social and Behavioral Sciences Approach is to reintroduce Bayesian inference and computing to a general social sciences audience. This is an important contribution-one that will make demand for this book high  Jeff Gill has gone some way toward reinventing the graduate-level methodology textbook  Gill's treatment of the practicalities of convergence is a real service  new users of the technique will appreciate this material.  the inclusion of material on hierarchical modeling at first seems unconventional; its use in political science, while increasing, has been limited. However, Bayesian inference and MCMC methods are well-suited to these types of problems, and it is exactly these types of treatments that push the discipline in new directions. As noted, a number of monographs have appeared recently to reintroduce Bayesian inference to a new generation of computer-savvy statisticians.  However, Gill achieves what these do not: a quality introduction and reference guide to Bayesian inference and MCMC methods that will become a standard in political methodology.
-The Journal of Politics, November 2003

Product Description
The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings. New to the Second Edition · Two chapters on Markov chain Monte Carlo (MCMC) that cover ergodicity, convergence, mixing, simulated annealing, reversible jump MCMC, and coupling · Expanded coverage of Bayesian linear and hierarchical models · More technical and philosophical details on prior distributions · A dedicated R package (BaM) with data and code for the examples as well as a set of functions for practical purposes such as calculating highest posterior density (HPD) intervals Requiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises.

Reader Reviews
This review is from: Bayesian Methods: A Social and Behavioral Sciences Approach (Hardcover) Jeff Gill is a statistician and a programming geek. He writes code in R and S. This book is an introduction to Bayesian methods for social scientists with the primary goal of making Bayesian methods accessible and used in that discipline. I discovered Jeff when I took a course from George Casella on Markov Chain Monte Carlo (MCMC). Jeff helped George teach the ins and outs of BUGS and BOA and CODA all common and important tools for the implementation and understanding of MCMC. In the course Jeff presented material from examples in this book (which was not yet out when I took the course). I knew then that I wanted to get this book first chance I got! I am a statistician and this is a great reference for statisticians and biostatisticians who are also finding Bayesian methods and MCMC very useful. The book is designed for social scientists but is good for everyone wanting to do sophisticated Bayesian analyses! Comment | | (Report this)