Bayesian Statistical Modelling (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section) [Hardcover]

Professor Peter Congdon (Author)

Book Description

ISBN-10: 0471496006 | ISBN-13: 978-0471496007 | Publication Date: May 2, 2001 | Edition: 1

Bayesian methods draw upon previous research findings and combine them with sample data to analyse problems and modify existing hypotheses. The calculations are often extremely complex, with many only now possible due to recent advances in computing technology. Bayesian methods have as a result gained wider acceptance, and are applied in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Bayesian Statistical Modelling presents an accessible overview of modelling applications from a Bayesian perspective.
* Provides an integrated presentation of theory, examples and computer algorithms
* Examines model fitting in practice using Bayesian principles
* Features a comprehensive range of methodologies and modelling techniques
* Covers recent innovations in bayesian modelling, including Markov Chain Monte Carlo methods
* Includes extensive applications to health and social sciences
* Features a comprehensive collection of nearly 200 worked examples
* Data examples and computer code in WinBUGS are available via ftp
Whilst providing a general overview of Bayesian modelling, the author places emphasis on the principles of prior selection, model identification and interpretation of findings, in a range of modelling innovations, focussing on their implementation with real data, with advice as to appropriate computing choices and strategies.
Researchers in applied statistics, medical science, public health and the social sciences 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 good reference source for both researchers and students.

Editorial Reviews

Review

"I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and range of the discussions in contains?I can certainly recommend it..." (Short Book Reviews, Vol. 21, No. 3, December 2001)

"...aims to contribute to the development of accessible software methods for applying Bayesian methodology." (Zentralblatt MATH, Vol. 967, 2001/17)

"I would recommend this book to any industrial statistician as a good starting pint for learning about Bayesian methodology and also to those already familiar with Bayesian techniques as a helpful guide to developing proficiency in using BUGS software." (Technometrics, Vol. 44, No. 3, August 2002)

"...fills an important niche in the statistical literature and should be a vary valuable resource for students and professionals..." (Journal of Mathematical Psychology, 2002)

"...an excellent introductory book..." (Biometrics, June 2002)

"...has valuable resources for instructors, statisticians, and researchers..." (Journal of the American Statistical Association, March 2003)

From the Back Cover

Bayesian methods draw upon previous research findings and combine them with sample data to analyse problems and modify existing hypotheses. The calculations are often extremely complex, with many only now possible due to recent advances in computing technology. Bayesian methods have as a result gained wider acceptance, and are applied in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Bayesian Statistical Modelling presents an accessible overview of modelling applications from a Bayesian perspective.
* Provides an integrated presentation of theory, examples and computer algorithms
* Examines model fitting in practice using Bayesian principles
* Features a comprehensive range of methodologies and modelling techniques
* Covers recent innovations in bayesian modelling, including Markov Chain Monte Carlo methods
* Includes extensive applications to health and social sciences
* Features a comprehensive collection of nearly 200 worked examples
* Data examples and computer code in WinBUGS are available via ftp
Whilst providing a general overview of Bayesian modelling, the author places emphasis on the principles of prior selection, model identification and interpretation of findings, in a range of modelling innovations, focussing on their implementation with real data, with advice as to appropriate computing choices and strategies.
Researchers in applied statistics, medical science, public health and the social sciences 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 good reference source for both researchers and students.

See all Editorial Reviews

Product Details

• Hardcover: 531 pages
• Publisher: Wiley; 1 edition (May 2, 2001)
• Language: English
• ISBN-10: 0471496006
• ISBN-13: 978-0471496007
• Product Dimensions: 9.2 x 6.3 x 1.4 inches
• Shipping Weight: 2.2 pounds
• Average Customer Review: 4.5 out of 5 stars  See all reviews (4 customer reviews)
• Amazon Best Sellers Rank: #2,581,977 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

24 of 25 people found the following review helpful:

5.0 out of 5 stars nice coverage of Bayesian methods, January 23, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Bayesian Statistical Modelling (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section) (Hardcover)

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.

19 of 22 people found the following review helpful:

3.0 out of 5 stars It is not clear what the purpose of this book is, December 7, 2007

By 

Alexander C. Zorach "Alex Zorach" (Lancaster, PA) - See all my reviews
(REAL NAME)   

This review is from: Bayesian Statistical Modelling (Wiley Series in Probability and Statistics) (Hardcover)

One of the reasons that I am giving this book such a low review is that it's not clear from the book's title, preface, or blurb on the back cover, nor from the first chapter what the purpose of this book and its intended audience are. The back cover is outright misleading; it calls this book an "introductory book on Bayesian modeling techniques". In my opinion, this book seems to be aimed at researchers who already have a strong mastery of most of the techniques used in this book and want a comprehensive overview of the literature as well as a philosophically-sound guide of how to put this theory to use.

The book is quite well-written. The prose is clear, and the author uses just the right amount of mathematical notation and graphs. The author has a comprehensive understanding of the literature, and gives numerous and appropriate references both to justify his points, and to point the reader to further reading.

My objection to this book is that it is not a good place to go to learn any of the material--especially the theoretical material. There is not much exposition of the theory; in contrast to many books, this book provides a good justification of the "why" but a poor explanation of the "how". While I deeply appreciate the "why", I am not satisfied without both. One can have a fairly solid general background in statistics and yet still have trouble understanding this book: this book requires a solid prior background in Bayesian inference, MCMC sampling, and the appropriate areas of regression. This book would not be very useful to people who did not already know most of the material contained in it.

In my opinion, this book would be greatly improved by being more honest and forward about the purpose, intended audience, and required background. I would also deeply appreciate it if the authors would do a better job of pointing to references which are better places to learn this material--most of the references are to the primary literature, and although they do reference a few very good textbooks, there are a ton of key subjects for which they do not point the reader to any good learning sources. I might be convinced to give this book five stars if the author could address these shortcomings.

0 of 1 people found the following review helpful:

5.0 out of 5 stars Outstanding book!, June 7, 2010

By 

LOV - See all my reviews

Amazon Verified Purchase

This review is from: Bayesian Statistical Modelling (Wiley Series in Probability and Statistics) (Hardcover)

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.