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Markov chain Monte Carlo : stochastic simulation for Bayesian inference.

Author: Dani Gamerman; Hedibert Freitas Lopes
Publisher: Boca Raton : Taylor & Francis, 2006.
Series: Texts in statistical science, v. 68.
Edition/Format:   Print book : English : 2nd ed.View all editions and formats
Summary:
"Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an Internet site that provides the code, written in R and WinBUGS, used in many of the previously existing
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Dani Gamerman; Hedibert Freitas Lopes
ISBN: 1584885874 9781584885870
OCLC Number: 64596046
Description: xvii, 323 pages : illustrations ; 24 cm.
Contents: 1. Stochastic simulation --
2. Bayesian inference --
3. Approximate methods of inference --
4. Markov chains --
5. Gibbs sampling --
6. Metropolis-Hastings algorithms --
7. Further topics in MCMC.
Series Title: Texts in statistical science, v. 68.
More information:

Abstract:

Incorporating changes in theory and highlighting various applications, this book presents a comprehensive introduction to the methods of Markov Chain Monte Carlo (MCMC) simulation technique. It  Read more...

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