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Zusatztext "The new edition of the book! with its updated and additional materials! is still a great choice as at textbook for Bayesian computation and inference courses in a graduate program in computational and applied statistics. It will also be considered as one of the best textbooks for a Bayesian computational course to nonstatisticians! including social scientists and engineers." - Debajyoti Sinha! Florida State University! in JASA! March 2009"The second edition of this book is well written and builds on the first edition ? The addition of an associated website is a valuable resource that contains many R scripts! allowing readers to quickly and easily test different approaches on their desired models with minimal effort. Coupling this with the depth of examples and references provided! this text provides an excellent first graduate text on MCMC methods. ? The book is certainly another fine addition on the literature on MCMC and should be used by anyone interested in gaining a solid foundation in MCMC methods and algorithms. ?" -Gareth Peters (University of New South Wales)! Statistics in Medicine! 2008 "? 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)! ISI Short Book Reviews "?The book may be quite useful as a first book on MCMC. ? The treatment is nontechnical! easily read! and may be a good starting point for a statistician with little or no prior knowledge of MCMC. There is also nonstandard material. I found the material on dynamical models (including non-Gaussian ones) particularly interesting. ?" -Søren Feodor Nielsen (University of Copenhagen)! Journal of Applied Statistics! Vol. 34! No. 7! December 2007 "?The book does have an impressive set of exercises ? it would be appropriate for a course that wants to focus on using MCMC to solve applied Bayesian inference problems." -Galin L. Jones! Mathematical Reviews! 2007j Praise for the First Edition: "?a must for every research library! and should be given serious consideration for use as a graduate text." -ISI Short Book Reviews "?nicely focused! elementary-level coverage?makes this book a suitable choice for an introductory course." -Journal of the ASA! March 2000 Informationen zum Autor Dani Gamerman, Hedibert F. Lopes Klappentext 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. Zusammenfassung 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. Inhaltsverzeichnis Introduction. Bayesian Inference. Approximate Methods of Inference. Markov Chains. MCMC. Gibbs Sampling. Metropolis-Hastings Algorithms. Further Topics in MCMC....
Inhaltsverzeichnis
Introduction. Bayesian Inference. Approximate Methods of Inference. Markov Chains. MCMC. Gibbs Sampling. Metropolis-Hastings Algorithms. Further Topics in MCMC.
Bericht
"The new edition of the book, with its updated and additional materials, is still a great choice as at textbook for Bayesian computation and inference courses in a graduate program in computational and applied statistics. It will also be considered as one of the best textbooks for a Bayesian computational course to nonstatisticians, including social scientists and engineers."
- Debajyoti Sinha, Florida State University, in JASA, March 2009
"The second edition of this book is well written and builds on the
first edition ... The addition of an associated website is a valuable resource that contains many R scripts, allowing readers to quickly and easily test different approaches on their desired models with minimal effort. Coupling this with the depth of examples and references provided, this text provides an excellent first graduate text on MCMC methods. ... The book is certainly another fine addition on the literature on MCMC and should be used by anyone interested in gaining a solid foundation in MCMC methods and algorithms. ..."
-Gareth Peters (University of New South Wales), Statistics in Medicine, 2008
"... 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), ISI Short Book Reviews
"...The book may be quite useful as a first book on MCMC. ... The treatment is nontechnical, easily read, and may be a good starting point for a statistician with little or no prior knowledge of MCMC. There is also nonstandard material. I found the material on dynamical models (including non-Gaussian ones) particularly interesting. ..."
-Søren Feodor Nielsen (University of Copenhagen), Journal of Applied Statistics, Vol. 34, No. 7, December 2007
"...The book does have an impressive set of exercises ... it would be appropriate for a course that wants to focus on using MCMC to solve applied Bayesian inference problems."
-Galin L. Jones, Mathematical Reviews, 2007j
Praise for the First Edition:
"...a must for every research library, and should be given serious consideration for use as a graduate text."
-ISI Short Book Reviews
"...nicely focused, elementary-level coverage...makes this book a suitable choice for an introductory course."
-Journal of the ASA, March 2000
