Historically, Bayesian solutions were computed as needed in formal languages(Fortran, C,java,etc…) and later in high level solutions like Matlab,Gauss,SAS/IML and others. Then Winbugs came along and offered a higher level interface, similar to what Matlab did for linear algebra syntax and functionality, but closer in spirit to the notation used by Statisticans to depict multilevel probability based models. While all of these still have their pros and cons, we find now an explosion of Bayesian solutions implemented in R with the benefit of object orientation. If one takes a look at the “CRAN Task View: Bayesian Inference” page on the R site maintained by Jong Hee Park, one will find 60+ packages with numerous solutions to many standard statistical modeling problems. Of the many listed, note the package BAS for Bayesian Model Averaging in linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner’s gprior or mixtures of gpriors corresponding to the ZellnerSiow Cauchy Priors or the Liang et al hyperg priors. The stochastic search capability allows for model specification searches that would not have been possible a few years ago with the ease that is now possible.

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