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Bayesian Computation with R

Albert, J

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Book Information
Edition:	2nd
Publisher:	Springer Nature
ISBN:	0-387-92298-9 (0387922989)
ISBN-13:	978-0-387-92298-0 (9780387922980)
Binding:	E E Book + ProQuest Ebook Central
Copyright:	2009
Publish Date:	08/09
Weight:	0.00 Lbs.
Subject Class:	MTH (Mathematics)
Return Policy:	Non-Returnable.
Table Of Contents:	View

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Abstract:

Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling. Algorithms written in R are used to develop Bayesian tests and assess Bayesian models by use of the posterior predictive distribution. The use of R to interface with WinBUGS, a popular MCMC computing language, is described with several illustrative examples.

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