Ntzoufras Bayesian Modeling Using WinBUGS

1. Auflage 2011
ISBN: 978-1-118-21035-2
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 520 Seiten, E-Book

Reihe: Wiley Series in Computational Statistics

A hands-on introduction to the principles of Bayesian modelingusing WinBUGS
Bayesian Modeling Using WinBUGS provides an easilyaccessible introduction to the use of WinBUGS programmingtechniques in a variety of Bayesian modeling settings. The authorprovides an accessible treatment of the topic, offering readers asmooth introduction to the principles of Bayesian modeling withdetailed guidance on the practical implementation of keyprinciples.
The book begins with a basic introduction to Bayesian inferenceand the WinBUGS software and goes on to cover key topics,including:
* Markov Chain Monte Carlo algorithms in Bayesian inference
* Generalized linear models
* Bayesian hierarchical models
* Predictive distribution and model checking
* Bayesian model and variable evaluation
Computational notes and screen captures illustrate the use ofboth WinBUGS as well as R software to apply the discussedtechniques. Exercises at the end of each chapter allow readers totest their understanding of the presented concepts and all datasets and code are available on the book's related Web site.
Requiring only a working knowledge of probability theory andstatistics, Bayesian Modeling Using WinBUGS serves as anexcellent book for courses on Bayesian statistics at theupper-undergraduate and graduate levels. It is also a valuablereference for researchers and practitioners in the fields ofstatistics, actuarial science, medicine, and the social scienceswho use WinBUGS in their everyday work.

Autoren/Hrsg.

Ntzoufras, Ioannis

Über Autor(innen)

Ioannis Ntzoufras, PhD, is Assistant Professor of Statistics at Athens University of Economics and Business (Greece). Dr. Ntzoufras has published numerous journal articles in his areas of research interest, which include Bayesian statistics, statistical analysis and programming, and generalized linear models.