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Praise for the Second Edition:
The book will be very suitable for students of social science … The reference list is carefully compiled; it will be very useful for a well-motivated reader. Altogether it is a very readable book, based on solid scholarship and written with conviction, gusto, and a sense of fun.
—International Statistical Review (2009), 77, 2
The second edition of Bayesian Methods: A Social and Behavioral Sciences Approach is a major update from the original version. … The result is a general audience text suitable for a first course in Bayesian statistics at the upper undergraduate level for highly quantitative students or at the graduate level for students in a wider variety of fields. … Of the texts I have tried so far in [my] class, Gill’s book has definitely worked the best for me. … this book fills an important market segment for classes where the canonical Bayesian texts are a bit too advanced. The emphasis is on using Bayesian methods in practice, with topics introduced via higher-level discussions followed by implementation and theory. …
—Herbert K.H. Lee, University of California, Santa Cruz, The American Statistician, November 2008
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.
—S.V. Subramanian, Harvard School of Public Health
One of the 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