Bayesian Methods

Bayesian Methods : A Social and Behavioral Sciences Approach

3.72 (11 ratings by Goodreads)
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Description

Despite increasing interest in Bayesian approaches, especially across the social sciences, it has been virtually impossible to find a text that introduces Bayesian data analysis in a manner accessible to social science students. The Bayesian paradigm is ideally suited to the type of data analysis they will have to perform, but the associated mathematics can be daunting. "Bayesian Methods: A Social and Behavioral Sciences Approach" presents the basic principles of Bayesian statistics in a treatment designed specifically for students in the social sciences and related fields.Requiring few prerequisites, it first introduces Bayesian statistics and inference with detailed descriptions of setting up a probability model, specifying prior distributions, calculating a posterior distribution, and describing the results. This is followed by explicit guidance on assessing model quality and model fit using various diagnostic techniques and empirical summaries. Finally, it introduces hierarchical models within the Bayesian context, which leads naturally to Markov Chain Monte Carlo computing techniques and other numerical methods. The author emphasizes practical computing issues, includes specific details for Bayesian model building and testing, and uses the freely available R and BUGS software for examples and exercise problems. The result is an eminently practical text that is comprehensive, rigorous, and ideally suited to teaching future empirical social scientists.show more

Product details

  • Hardback | 480 pages
  • 152.4 x 241.3 x 30.48mm | 725.74g
  • Taylor & Francis Inc
  • CRC Press Inc
  • Bosa Roca, United States
  • English
  • 27 black & white illustrations, 24 black & white tables
  • 1584882883
  • 9781584882886

Review quote

"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 "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 highJeff Gill has gone some way toward reinventing the graduate-level methodology textbook Gill's treatment of the practicalities of convergence is a real servicenew 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 2003show more

Table of contents

Background and Introduction. Likelihood Inference and the Generalized Linear Model. The Bayesian Setup. The Normal and Student's-T Models. The Bayesian Prior. Assessing Model Quality. Bayesian Hypothesis Testing and the Bayes Factor. Bayesian Posterior Simulation. Basics of Markov Chain Monte Carlo. Bayesian Hierarchical Models. Practical Markov Chain Monte Carlo.show more

Rating details

11 ratings
3.72 out of 5 stars
5 36% (4)
4 27% (3)
3 18% (2)
2 9% (1)
1 9% (1)
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