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Book Title: Markov Chain Monte Carlo (Chapman & Hall/CRC Texts in Statistical

ISBN: 9781584885870

Über dieses Produkt

Product Identifiers

Publisher

CRC Press LLC

ISBN-10

1584885874

ISBN-13

9781584885870

eBay Product ID (ePID)

50935070

Product Key Features

Number of Pages

342 Pages

Publication Name

Markov Chain Monte Carlo : Stochastic Simulation for Bayesian Inference, Second Edition

Language

English

Subject

Probability & Statistics / Stochastic Processes, Probability & Statistics / General

Publication Year

2006

Type

Textbook

Author

Hedibert F. Lopes, Dani Gamerman

Subject Area

Mathematics

Series

Chapman and Hall/Crc Texts in Statistical Science Ser.

Format

Hardcover

Dimensions

Item Height

0.9 in

Item Weight

21.7 Oz

Item Length

9.5 in

Item Width

6.6 in

Additional Product Features

Edition Number

Intended Audience

College Audience

LCCN

2006-044491

Dewey Edition

Illustrated

Yes

Dewey Decimal

519.542

Edition Description

Revised edition,New Edition

Table Of Content

Introduction Stochastic simulation Introduction Generation of Discrete Random Quantities Generation of Continuous Random Quantities Generation of Random Vectors and Matrices Resampling Methods Exercises Bayesian Inference Introduction Bayes' Theorem Conjugate Distributions Hierarchical Models Dynamic Models Spatial Models Model Comparison Exercises Approximate methods of inference Introduction Asymptotic Approximations Approximations by Gaussian Quadrature Monte Carlo Integration Methods Based on Stochastic Simulation Exercises Markov chains Introduction Definition and Transition Probabilities Decomposition of the State Space Stationary Distributions Limiting Theorems Reversible Chains Continuous State Spaces Simulation of a Markov Chain Data Augmentation or Substitution Sampling Exercises Gibbs Sampling Introduction Definition and Properties Implementation and Optimization Convergence Diagnostics Applications MCMC-Based Software for Bayesian Modeling Appendix 5.A: BUGS Code for Example 5.7 Appendix 5.B: BUGS Code for Example 5.8 Exercises Metropolis-Hastings algorithms Introduction Definition and Properties Special Cases Hybrid Algorithms Applications Exercises Further topics in MCMC Introduction Model Adequacy Model Choice: MCMC Over Model and Parameter Spaces Convergence Acceleration Exercises References Author Index Subject Index

Synopsis

While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. 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 and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration. Major changes from the previous edition: - More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms - Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection - Discussion of computation using both R and WinBUGS - Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web - Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses., While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. 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 and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration. Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses., Presenting a comprehensive introduction to the methods of this valuable simulation technique, this second edition includes new chapters on Gibbs sampling and Metropolis-Hastings algorithms. It incorporates all the recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, and more. With additional exercises and selected solutions within the text, it offers all data sets and software for download from the Web.

LC Classification Number

QA279.5.G36 2006

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