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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Bibliographic Information

Book Title: Bayesian Computation with R
Authors: Jim Albert
Series Title: Use R!
DOI: https://doi.org/10.1007/978-0-387-92298-0
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Softcover ISBN: 978-0-387-92297-3Published: 15 May 2009
eBook ISBN: 978-0-387-92298-0Published: 20 April 2009
Series ISSN: 2197-5736
Series E-ISSN: 2197-5744
Edition Number: 2
Number of Pages: XII, 300
Topics: Probability Theory and Stochastic Processes, Computational Mathematics and Numerical Analysis, Mathematical Software, Statistical Theory and Methods, Simulation and Modeling, Visualization

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Table of contents (11 chapters)

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