Markov Chain Monte Carlo - Gamerman Dani; Lopes Hedibert F. | Libro Chapman And Hall/Crc 05/2006 - HOEPLI.it


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

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Dettagli

Genere:Libro
Lingua: Inglese
Pubblicazione: 05/2006
Edizione: Edizione nuova, 2° edizione





Note Editore

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.




Sommario

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




Trama

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.







Altre Informazioni

ISBN:

9781584885870

Condizione: Nuovo
Collana: Chapman & Hall/CRC Texts in Statistical Science
Dimensioni: 9.25 x 6.25 in Ø 1.35 lb
Formato: Copertina rigida
Illustration Notes:44 b/w images and 15 tables
Pagine Arabe: 342






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