Bayesian Methods

Jeff Gill is a professor in the Department of Political Science, the Division of Biostatistics, and the Department of Surgery (Public Health Sciences) at Washington University. He is the author of several books and has published numerous research articles. His research applies Bayesian modeling and data analysis to questions in general social science quantitative methodology, political behavior and institutions, and medical/health data analysis using computationally intensive tools. He received his B.A. from UCLA, MBA from Georgetown University, Ph.D. from American University, and Post-Doctorate from Harvard University.

Praise for the Third Edition:Bayesian Methods covers a broad yet essential scope of topics necessary for one to understand and conduct applied Bayesian analysis. The numerous social science examples should resonate with the target audience, and the availability of the code and data in an R package, BaM, further enhances the appeal of the book.—The American Statistician, 2016Praise 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, 2The 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 2008Praise 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 HealthOne 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

• Background and Introduction. Specifying Bayesian Models. The Normal and Student's-t Models. The Bayesian Linear Model. The Bayesian Prior. Assessing Model Quality. Bayesian Hypothesis Testing and the Bayes' Factor. Bayesian Decision Theory. Monte Carlo and Related Iterative Methods. Basics of Markov Chain Monte Carlo. Implementing Bayesian Models with Markov Chain Monte Carlo. Bayesian Hierarchical Models. Some Markov Chain Monte Carlo Theory. Utilitarian Markov Chain Monte Carlo. Advanced Markov Chain Monte Carlo. Appendices. References. Indices.