Bayesian Methods

The third edition of this bestseller focuses more on implementation details of the procedures and less on justifying procedures. It includes new chapters on Bayesian decision theory and the practical implementation of MCMC methods using the BUGS software. It also expands the chapter on hierarchical models, presents many new applications from a variety of social science disciplines, and doubles the number of exercises. The author's website provides new datasets, code, and procedures for calling BUGS packages from R.

An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists

Now that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of the procedures and less on justifying procedures. The expanded examples reflect this updated approach.

New to the Third Edition

• A chapter on Bayesian decision theory, covering Bayesian and frequentist decision theory as well as the connection of empirical Bayes with James-Stein estimation
• A chapter on the practical implementation of MCMC methods using the BUGS software
• Greatly expanded chapter on hierarchical models that shows how this area is well suited to the Bayesian paradigm
• Many new applications from a variety of social science disciplines
• Double the number of exercises, with 20 now in each chapter

• Updated BaM package in R, including new datasets, code, and procedures for calling BUGS packages from R

This bestselling, highly praised text continues to be suitable for a range of courses, including an introductory course or a computing-centered course. It shows students in the social and behavioral sciences how to use Bayesian methods in practice, preparing them for sophisticated, real-world work in the field.

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, 2016 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 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

Autorentext

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.

Inhalt

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.