Bayesian Methods for the Social and Behavioral Sciences

ISBN-10: 1584882883

ISBN-13: 9781584882886

Edition: 2002

Authors: Jeff Gill

List price: $79.95
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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.
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Book details

List price: $79.95
Copyright year: 2002
Publisher: CRC Press LLC
Publication date: 5/29/2002
Binding: Hardcover
Pages: 480
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 1.738
Language: English

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
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