9781584885870 - markov chain monte carlo: stochastic simulation for bayesian inference, second edition: 68 (chapman & hall/crc texts in statistical science) de gamerman, dani; lopes, hedibert f. (21 resultados)
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Serie: Chapman & Hall/CRC Texts in Statistical Science, Libro 16 de 59. Libro 16 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Editorial: Chapman and Hall/CRC, 2006
Serie: Chapman & Hall/CRC Texts in Statistical Science, Libro 16 de 59. Libro 16 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Editorial: Chapman and Hall/CRC, 2006
Serie: Chapman & Hall/CRC Texts in Statistical Science, Libro 16 de 59. Libro 16 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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hardcover. Condición: New. In shrink wrap. Looks like an interesting title.
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Serie: Chapman & Hall/CRC Texts in Statistical Science, Libro 16 de 59. Libro 16 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Serie: Chapman & Hall/CRC Texts in Statistical Science, Libro 16 de 59. Libro 16 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Serie: Chapman & Hall/CRC Texts in Statistical Science, Libro 16 de 59. Libro 16 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Hardback. Condición: New. 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.
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Serie: Chapman & Hall/CRC Texts in Statistical Science, Libro 16 de 59. Libro 16 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Hardcover. Condición: Brand New. 2nd edition. 323 pages. 9.25x6.25x0.75 inches. In Stock.
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Hardback. Condición: New. 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.
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Serie: Chapman & Hall/CRC Texts in Statistical Science, Libro 16 de 59. Libro 16 de 59 - Chapman & Hall/CRC Texts in Statistical Science
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. I…ncorporating 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. 342 pp. Englisch.
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dani Gamerman, Hedibert F. LopesWhile 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 sol…ution of inference problems has increased b.
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Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Incorp…orating 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.
