Bayesian Computation with R

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Springer Science & Business Media, Apr 20, 2009 - Mathematics - 300 pages
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There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).
 

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This book is DRM'd. Funny, given that it's about the use of open source software.

Contents

An Introduction to R
1
Introduction to Bayesian Thinking
19
SingleParameter Models
38
Multiparameter Models
63
Introduction to Bayesian Computation
87
Markov Chain Monte Carlo Methods
116
Hierarchical Modeling
153
Model Comparison
181
Regression Models
205
Gibbs Sampling
235
Using R to Interface with WinBUGS
265
References
287
Index
292
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About the author (2009)

Albert is Professor of Mathematics and Statistics at Bowling Green State University. He has served as Chair of the Sports Section of the American Statistical Association.