Qty:1
  • List Price: $129.95
  • Save: $41.04 (32%)
Only 2 left in stock (more on the way).
Ships from and sold by Amazon.com.
Gift-wrap available.
Add to Cart
Used: Very Good | Details
Sold by BigHeartedBooks
Condition: Used: Very Good
Comment: Book is in very good condition, there may be some minor wear from a prior reader or two but very good books are in excellent condition. Super fast shipping is available and we offer a money back guarantee.
Access codes and supplements are not guaranteed with used items.
Add to Cart
Trade in your item
Get a $27.43
Gift Card.
Have one to sell? Sell on Amazon
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See all 2 images

Markov Chain Monte Carlo in Practice (Chapman & Hall/CRC Interdisciplinary Statistics) Hardcover – January 1, 1996

ISBN-13: 978-0412055515 ISBN-10: 0412055511 Edition: Softcover reprint of the original 1st ed. 1996

Buy New
Price: $88.91
29 New from $85.00 28 Used from $51.55
Rent from Amazon Price New from Used from
eTextbook
"Please retry"
$30.53
Hardcover
"Please retry"
$88.91
$85.00 $51.55
Paperback
"Please retry"
Free%20Two-Day%20Shipping%20for%20College%20Students%20with%20Amazon%20Student


Frequently Bought Together

Markov Chain Monte Carlo in Practice (Chapman & Hall/CRC Interdisciplinary Statistics) + Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) + Handbook of Markov Chain Monte Carlo (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)
Price for all three: $236.01

Buy the selected items together

NO_CONTENT_IN_FEATURE
Save up to 90% on Textbooks
Rent textbooks, buy textbooks, or get up to 80% back when you sell us your books. Shop Now

Product Details

  • Series: Chapman & Hall/CRC Interdisciplinary Statistics (Book 2)
  • Hardcover: 512 pages
  • Publisher: Chapman and Hall/CRC; Softcover reprint of the original 1st ed. 1996 edition (January 1, 1996)
  • Language: English
  • ISBN-10: 0412055511
  • ISBN-13: 978-0412055515
  • Product Dimensions: 9.5 x 6.2 x 1.2 inches
  • Shipping Weight: 1.9 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #1,041,612 in Books (See Top 100 in Books)

Customer Reviews

4.3 out of 5 stars
5 star
2
4 star
0
3 star
1
2 star
0
1 star
0
See all 3 customer reviews
Share your thoughts with other customers

Most Helpful Customer Reviews

31 of 34 people found the following review helpful By Michael R. Chernick on February 8, 2008
Format: Hardcover
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.
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
14 of 40 people found the following review helpful By Falling Maple on May 5, 2005
Format: Hardcover
First, I'll like to comment on the termiology. I'm PhD specializing in stochastic simulation in operations researcn and I've found the book is written in a language that's not quite standard (it might have something to do with his background in Statistics). Some people may argue that "names" are just "names" but it could cause confusion. And, in the chapter of stochastic approximation, the author failed to mention a couple of well-known existing methodology (somehow show a poor literature review in the field.) Strong emphasis has been given on importance sampling on that particular chapter, but author failed to mention in what context will importance sampling work. If you assume Bayesian approach and have prior on the parameters, then it works. But, if you're a frequentist, it's not necessarily working for your model.

Going back to the first chapter, I found the construction of MCMC is presented much more clearly in Sheldon Ross's Probability Model rather than this book.
1 Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again
11 of 43 people found the following review helpful By chris_gordon1@rocketmail.com on October 25, 1997
Format: Hardcover
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
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again

Customer Images

Search