Bayesian Computation With R (use R!)
by Jim Albert /
2009 / English / PDF
2.5 MB Download
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).
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).