Bayesian Computation with R (Use R!)

Bayesian Computation with R (Use R!)

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).
Country USA
Brand Springer
Manufacturer Springer
Binding Paperback
ItemPartNumber 26383181
ReleaseDate 2009-05-15
UnitCount 1
EANs 9780387922973

Country	USA
Brand	Springer
Manufacturer	Springer
Binding	Paperback
ItemPartNumber	26383181
ReleaseDate	2009-05-15
UnitCount	1
EANs	9780387922973

Bayesian Computation with R (Use R!)

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