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

Dani Gamerman, Hedibert F. Lopes

May 10, 2006 by Chapman and Hall/CRC
Reference - 342 Pages - 44 B/W Illustrations
ISBN 9781584885870 - CAT# C5874
Series: Chapman & Hall/CRC Texts in Statistical Science

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Features

  • Covers techniques used to perform Bayesian inference based on stochastic simulation
  • Presents basic, direct simulation operations for those not familiar with them
  • Provides an understanding of the properties of Markov chains and the relevant results
  • Discusses Gibbs sampling and includes examples of a number of situations including models with hierarchical structure, models for spatial data and models with a dynamic setting
  • Explores the Gibbs sampling and Metropolis-Hastings algorithms and presents numerical comparisons
  • Includes coverage of alternative models that can be used as auxiliary devices in designing a MCMC method for a particular model
  • Summary

    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.