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Full Description
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.
Contents
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.
