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Markov Chain Monte Carlo - Stochastic Simulation for Bayesian Inference, Second Edition (Hardcover, 2nd edition)
Loot Price: R2,837
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Markov Chain Monte Carlo - Stochastic Simulation for Bayesian Inference, Second Edition (Hardcover, 2nd edition)
Series: Chapman & Hall/CRC Texts in Statistical Science
Expected to ship within 12 - 17 working days
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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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