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Title: Bayesian Statistical Modelling (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section) by Peter Congdon ISBN: 0-471-49600-6 Publisher: John Wiley & Sons Pub. Date: 22 June, 2001 Format: Hardcover Volumes: 1 List Price(USD): $95.00 |
Average Customer Rating: 4 (2 reviews)
Rating: 4
Summary: Use it against spam?
Comment: One specific application of Bayesian approaches has recently become hot. It has been claimed by some that a way to attack spam in email is to use Bayesian filters. If that is your inclination, you may want to check out this book.
It has a solid, technical explanation of Bayesians and is replete with several examples. Some parts may be heavy going, depending on your mathematical background. But worth it eventually.
The problem is, if you do develop a Bayesian filter for spam and use it, you may find that there are fundamental limitations, due to broadening, and to spammers actively counterattacking Bayesians.
The examples in the book of applications all involve cases where the system being modelled does not change significantly; otherwise the Bayesian will have to be retrained on new data. But, and more importantly, the examples do not treat the case where the system can change in a way to deliberately defeat the Bayesian.
Rating: 4
Summary: nice recent text on Bayesian methods with many applications
Comment: 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.
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Title: Applied Bayesian Modelling (Wiley Series in Probability and Statistics) by Peter Congdon ISBN: 0471486957 Publisher: John Wiley & Sons Pub. Date: 22 April, 2003 List Price(USD): $95.00 |
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Title: Bayesian Data Analysis, Second Edition by Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin ISBN: 158488388X Publisher: Chapman & Hall/CRC Pub. Date: 29 July, 2003 List Price(USD): $59.95 |
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Title: Markov Chain Monte Carlo in Practice by W. R. Gilks, S. Richardson, D. J. Spiegelhalter ISBN: 0412055511 Publisher: Chapman & Hall/CRC Pub. Date: 01 December, 1995 List Price(USD): $89.95 |
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Title: Bayes and Empirical Bayes Methods for Data Analysis, Second Edition by Bradley P. Carlin, Thomas A. Louis, Bradley Carlin ISBN: 1584881704 Publisher: Chapman & Hall/CRC Pub. Date: 22 June, 2000 List Price(USD): $59.95 |
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Title: Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics) by Ming-Hui Chen, Qi-Man Shao, Joseph George Ibrahim ISBN: 0387989358 Publisher: Springer-Verlag Pub. Date: 01 January, 2000 List Price(USD): $89.95 |
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