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.
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.
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.
Inhaltsangabe
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.
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.
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