[R] Getting Started with Bayesian MCMC

Shige Song shigesong at gmail.com
Wed Apr 14 02:14:55 CEST 2010

Hi Ben,

Before you begin playing with BUGS/JAGS, there are several native R
packages that deal with a wide variety of Bayesian models that worth
considering. Among many others, I find MCMCpack, DPpackage, and
MCMCglmm very useful (and convenient).


On Tue, Apr 13, 2010 at 7:49 PM, Ben <misc7 at emerose.org> wrote:
> Hi all,
> I would like to start to use R's MCMC abilities to compute answers in
> Bayesian statistics.  I don't have any specific problems in mind yet,
> but I would like to be able to compute/sample posterior probabilities
> for low-dimensional custom models, as well as handle "standard"
> Bayesian cases like linear regression and hierarchical models.
> R clearly has a lot of abilities in this area:
>    http://cran.r-project.org/web/views/Bayesian.html
> --enough to be confusing!  For instance, there are apparently three
> separate interfaces to JAGS, and I'm not even sure I want/need to
> interface to JAGS at all.
> Can someone please get me started?  Are there a handful of "must-have"
> packages and software that everyone (who uses MCMC regularly) uses?
> Any responses are appreciated,
> --
> Ben
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