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Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Markov Chain Monte Carlo (Chapman & Hall/CRC Texts in Statistical Science)
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Hardcover. Condition: New. 2nd Edition. This is a new hardcover second edition, first printing copy, no DJ, black spine, 323 pages with index.
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Markov Chain Monte Carlo (Chapman & Hall/CRC Texts in Statistical Science)
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
hardcover. Condition: New.
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Markov Chain Monte Carlo (Chapman & Hall/CRC Texts in Statistical Science)
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
hardcover. Condition: New. Brand new gift quality hardcover Please email for photos. Larger books or sets may require additional shipping charges. Books sent via US Postal.
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Markov Chain Monte Carlo (Chapman & Hall/CRC Texts in Statistical Science)
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
hardcover. Condition: New. In shrink wrap. Looks like an interesting title!
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Markov Chain Monte Carlo : Stochastic Simulation for Bayesian Inference
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Condition: New.
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Markov Chain Monte Carlo (Chapman & Hall/CRC Texts in Statistical Science)
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Condition: New.
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Markov Chain Monte Carlo (Chapman & Hall/CRC Texts in Statistical Science)
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Condition: New.
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Markov Chain Monte Carlo : Stochastic Simulation for Bayesian Inference
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Markov Chain Monte Carlo (Chapman & Hall/CRC Texts in Statistical Science)
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Markov Chain Monte Carlo : Stochastic Simulation for Bayesian Inference
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
£ 143
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Markov Chain Monte Carlo : Stochastic Simulation for Bayesian Inference
Published by Chapman and Hall/CRC, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Condition: As New. Unread book in perfect condition.
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Markov Chain Monte Carlo
Published by Taylor and Francis Inc, US, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
£ 179.05
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Add to basketHardback. Condition: 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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£ 152.24
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Add to basketHardcover. Condition: Brand New. 2nd edition. 323 pages. 9.25x6.25x0.75 inches. In Stock.
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Markov Chain Monte Carlo
Published by Taylor and Francis Inc, US, 2006
ISBN 10: 1584885874 ISBN 13: 9781584885870
Language: English
£ 164.51
Convert currency£ 65 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: 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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£ 125.49
Convert currency£ 42.53 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketGebunden. Condition: 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 solution of inference problems has increased b.
