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Bayesian Computation With R

Bayesian Computation With R - 07 edition

ISBN13: 978-0387713847

Cover of Bayesian Computation With R 07 (ISBN 978-0387713847)
ISBN13: 978-0387713847
ISBN10: 0387713840
Cover type:
Edition/Copyright: 07
Publisher: Springer-Verlag New York
Published: 2007
International: No

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Bayesian Computation With R - 07 edition

ISBN13: 978-0387713847

Jim Albert

ISBN13: 978-0387713847
ISBN10: 0387713840
Cover type:
Edition/Copyright: 07
Publisher: Springer-Verlag New York

Published: 2007
International: No
Summary
  • Introduces Bayesian modeling by use of computation using the R language
  • Presents the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems
  • Several examples illustrate the use of R to interface with WinBUGS


There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry.

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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