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| Available: 0 |
| List Price:$79.99 |
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| Edition: | 2nd |
| Publisher: | Springer Nature |
| ISBN: | 0-387-92297-0 (0387922970) |
| ISBN-13: | 978-0-387-92297-3 (9780387922973) |
| Binding: | Softcover |
| Copyright: | 2009 |
| Publish Date: | 08/09 |
| Weight: | 1.00 Lbs. |
| Pages: | 300 |
| Subject Class: | MTH (Mathematics) |
| Remarks: | Please allow additional 3-4 days for delivery |
| Return Policy: | Returns accepted up to 12 months provided no other recalls or return restrictions apply. |
| Table Of Contents: |
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| ProQuest Ebook Central: |
Available in ProQuest Ebook Central™ 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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