Bayesian Statistical Modelling

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...

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

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.