Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)

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9781584885870: Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Review:

... one of the most comprehensive and readable texts on stochastic simulation using the technique of Markov chain Monte Carlo. ... this second edition has been extensively updated to include the recent literature. New sections on spatial modeling and model adequacy have now been included, together with more illustrative material. Many of the computer codes written in R and WinBUGS ... are available for download from the web. This enhances the utility of the book, both as a reference for researchers and a text on modern Bayesian computation and Bayesian inference courses for students.
-C.M. O'Brien (CEFAS Lowestoft Laboratory, UK), Short Book Reviews

Read the reviews of the bestselling first edition:
...a must for every research library, and should be given serious consideration for use as a graduate text
-Short Book Reviews of the ISI

...nicely focused, elementary-level coverage...makes this book a suitable choice for an introductory course.
-Journal of the ASA, March, 2000

"Sobre este título" puede pertenecer a otra edición de este libro.

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Descripción Taylor Francis Inc, United States, 2006. Hardback. Estado de conservación: New. 2nd Revised edition. Language: English . Brand New Book. 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. Nº de ref. de la librería AAZ9781584885870

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Descripción Taylor Francis Inc, United States, 2006. Hardback. Estado de conservación: New. 2nd Revised edition. Language: English . Brand New Book. 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. Nº de ref. de la librería AAZ9781584885870

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Descripción Taylor & Francis Inc, 2006. Estado de conservación: New. 2006. 2nd Edition. Hardcover. 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 incorporates the developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection. Series: Chapman & Hall/CRC Texts in Statistical Science. Num Pages: 342 pages, 44 black & white illustrations, 15 black & white tables. BIC Classification: PBT. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 239 x 162 x 23. Weight in Grams: 634. . . . . . . Nº de ref. de la librería V9781584885870

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