Bayesian Statistical Modelling (Wiley Series in Probability and Statistics)

By Peter D. Congdon

Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics.

Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.

The second edition

Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader's knowledge and a supplementary website containing data sets and relevant programs.

Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.

Praise for the First Edition

"It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains."
--ISI - Short Book Reviews

"This is an excellent introductory book on Bayesian modelling techniques and data analysis."
--Biometrics

"The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods."
--Journal of Mathematical Psychology

• Format: Hardcover
• Language: English
• ISBN: 0470018755
• ISBN13: 9780470018750
• Release Date: January 2007
• Publisher: Wiley
• Length: 608 Pages
• Weight: 2.55 lbs.
• Dimensions: 10.0" x 1.5" x 6.9"

Customer Reviews

Outstanding book!

The book is simply awesome - it covers a very wide range of topics with pretty in-depth discussion in a practical way. Combined with the freely available WinBUGS programs on the author's site, this would be pretty hard to beat. I have read quite a few Bayesian analysis books and this is definitely one of the best or simply the best. Looking forward to reading the author's latest book just published.

nice coverage of Bayesian methods

Congdon presents a very nice and modern treatment of Bayesian methods and models emphasizing implementation using BUGS or WINBUGS. The book covers Bayesian models for regression including linear, log-linear, robust and nonparametric regression. Covers association and classification, mixture models, latent variables, problems of missing data, survival analysis, hierarchical models for pooling information, time series and other correlated data methods (e.g. spatial processes), multivariate analysis, growth curves and model assessment criteria. The book is loaded with techniques and applications covering a wide variety of topics with reasonable depth. It also has a very large bibliography with many very relevant and useful references. But there is also a negative side to the bibliography. It was not carefully proofread and there are some annoyances as you will see the same reference listed two, three or more times in the bibliography. Also for such a nice reference text it should have included an author index as well as an ordinary index. Gibbs sampling is one of the primary estimation techniques in the book but the details are put off until section 10.1 where we get a nice introduction to Gibbs sampling and also the Metropolis algorithm with several excellent references. This is a good book to start implementing Bayesian methods through the MCMC technique. It contains mostly medical applications which is a nice feature for biostatisticians.