- Hardcover: 408 pages
- Publisher: Springer; 1st ed. 2000. Corr. 2nd printing 2001 edition (5 Oct. 2001)
- Language: English
- ISBN-10: 0387989358
- ISBN-13: 978-0387989358
- Product Dimensions: 15.6 x 2.4 x 23.4 cm
- Amazon Bestsellers Rank: 3,257,079 in Books (See Top 100 in Books)
- See Complete Table of Contents
Monte Carlo Methods in Bayesian Computation (Springer Series in Statistics) Hardcover – 5 Oct 2001
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"This book combines the theory topics with good computer and application examples from the field of food science, agriculture, cancer and others. The volume will provide an excellent research resource for statisticians with an interest in computer intensive methods for modelling with different sorts of prior information."
A.V. Tsukanov in "Short Book Reviews", Vol. 20/3, December 2000
Inside This Book(Learn More)
Most Helpful Customer Reviews on Amazon.com (beta)
Section 1.1 of the text "Aims" provides the objectives of the book and compares it to the other recent major works. Basically, the authors say that Tanner (1996), Gilks, Richardson and Spiegelhalter (1996), Gamerman (1997), Robert and Casella (1999) and Gelfand and Smith (2000) all offer developments in MCMC sampling. So this text only provides a brief but hopefully sufficient introduction to MCMC sampling.
The main objective of the book is to develop more advanced Monte Carlo methods that speed up the computational time for specialized Bayesian problems. Problems of interest to the authors include estimating posterior means, modes and standard deviations, Bayesian equivalent of p-values, marginal posterior densities, marginal likelihoods, Bayes factors, posterior model probabilities, Bayesian credible intervals (the Bayes analogue to frequentist confidence intervals) and highest posterior probability density intervals.
Chapter 1 sets the stage. It provides the objectives, an outline of the rest of the book and a list of motivating examples that will be used throughout the text.
Chapter 2 then provides the brief introduction to MCMC sampling. Some theory is provided, many useful references are cited and several ideas are well illustrated through examples and figures.
Chapter 3 is also introductory in nature showing how the methods of Chapter 2 can be applied to obtain various estimates based on the approximated posterior probability distribution.
The rest of the book deals with specialized topics and techniques important to Bayesian inference. The book contains a wealth of theory and a good mix of applications and challenging research problems. The authors are experienced contributors to this literature.
It is intended as an advanced graduate course for Ph.D. statistics student in their second or third year of graduate study. It also will serve statistical researchers with an excellent reference both for the practice and development of Bayesian inference. Applications in the area of biostatistics are emphasized but the methods apply to Bayesian statistical inference in all fields.
If you want a more general introduction to Bayesian methods, then Gelman et al, Bayesian Data Analysis is excellent.
If you are unclear about the controversies and want to know why the Bayesian approach is correct, and the others are flat wrong, then read Ed Jaynes book.
So what is this book for. Well, I think you have to be a specialist, interested in further development of the techniques, and in the maths. As a previous reviewer has commented (correctly), in that case you probably have easy access to the journal literature and need to think carefully what extra benefits this book gives you.
The references get five stars. The book gives almost no new information, hence the two stars.
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