Bayesian Methods

A Social and Behavioral Sciences Approach, Third Edition
3. Auflage 2014
ISBN: 978-1-4398-6248-3
Verlag: Chapman and Hall/CRC

Buch, Englisch, 722 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1533 g

Reihe: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

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.

Zielgruppe

Graduate students, statisticians, political scientists, and other social scientists such as psychologists, sociologists, economists, and educators.

Autoren/Hrsg.

Gill, Jeff

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

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.

Über Autor(innen)

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.