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Title: Markov Chain Monte Carlo in Practice by W. R. Gilks, S. Richardson, D. J. Spiegelhalter ISBN: 0-412-05551-1 Publisher: CRC Press Pub. Date: 01 December, 1995 Format: Hardcover Volumes: 1 List Price(USD): $89.95 |
Average Customer Rating: 5 (3 reviews)
Rating: 5
Summary: Applications for Experts and Interested Laymen
Comment: I have a graduate level physics and mathematics background and found that the real-world applications discussed in this book enhanced my understanding of Monte Carlo models. While I knew the math cold, this thought provoking book helped me make further model enhancements and allowances for the ambiguities in the models.
Monte Carlo experts who want to apply their knowlege to finance should also read: "Options, Futures, and Other Derivatives (5th Edition) by John Hull; and "Credit Derivatives" (2nd Edition) by Janet Tavakoli.
Rating: 5
Summary: great collection of articles on applications
Comment: Gilks, Richardson and Spiegelhalter edited this marvelous collection of papers on applications of Markov Chain Monte Carlo methods. There has been a big payoff for Bayesians as this method has been a breakthrough for dealing with flexible prior distributions. Most (but not all) of the articles deal with Bayesian applications. The editors themselves start out with an introductory chapter that covers the basic ideas and sets the stage for the articles to come. They provide many references including several of the articles in this volume.
The list of authors is quite impressive and many interesting examples are presented. The editors themselves contribute to other chapters. Spiegelhalter and Gilks co-authored a chapter on a Hepatitis B case study with Best and Inskip. Gilks has a chapter on full conditional distributions and co-authors a chapter on strategies for improving the MCMC algorithms. Richardson contributes a chapter on measurement error.
George and McCulloch deal with the use of Gibbs sampling to choose variables in a model based on a Bayesian approach. Raftery also has a chapter on Bayesian approaches in hypothesis testing and model selection. Green covers image analysis. There are many others (25 chapters in all). This is a great reference for anyone interested in MCMC methods.
The BUGS (Bayesian inference Using Gibbs Sampling)software was developed by Spiegelhalter, Thomas, Best and Gilks to implement Gibbs sampling in a variety of contexts. They illustrate its use along with the diagnostic software CODA in the application in Chapter 2. It is also mentioned in various other chapters in the book. There is currently a version called winBUGS which is designed for Windows operating systems.
Before jumping into the use of MCMC a user would be well advised to study this book.
Rating: 5
Summary: Very Useful.
Comment: We recommend this book to anyone who is interested in learning MCMC methods. Contains a excellent selection of practical examples. Christopher Gordon and Steve Hirschowitz
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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: CRC Press Pub. Date: 29 July, 2003 List Price(USD): $59.95 |
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Title: Bayesian Statistical Modelling by Peter Congdon ISBN: 0471496006 Publisher: John Wiley & Sons Pub. Date: 18 April, 2001 List Price(USD): $95.00 |
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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: CRC Press Pub. Date: 22 June, 2000 List Price(USD): $59.95 |
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Title: Monte Carlo Statistical Methods by Christian P. Robert, George Casella ISBN: 038798707X Publisher: Springer Verlag Pub. Date: 13 August, 1999 List Price(USD): $94.00 |
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Title: Applied Bayesian Modelling by Peter Congdon ISBN: 0471486957 Publisher: John Wiley & Sons Pub. Date: 22 April, 2003 List Price(USD): $95.00 |
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