bates at stat.wisc.edu
Thu Mar 16 17:17:29 CET 2000
Martyn Plummer <plummer at iarc.fr> writes:
> On 16-Mar-00 John Logsdon wrote:
> > Does anyone know of any R coding/functions for MCMC approaches? I am
> > currently using BUGS but I wonder if the bazaar has produced anything? I
> > think I am pushing BUGS to it's limit and possibly past it at the moment.
> I don't think you will find anything as polished as BUGS, but the
> components are available for you to write your own custom MCMC program.
> You have to do a lot of work by hand that is normally done by BUGS, e.g.
> specifying full conditionals, writing the functions for logical
> nodes, recognizing conjugate distributions, tuning the Gibbs sampler, etc.
> * libnmath contained within R provides plenty of functions for simulating
> random variables from commonly used distributions. Brian Ripley has
> kindly modified this so that it can be built separately. Using this you
> can handle conjugate distributions.
> * Wally Gilks has made publically available software for doing Adaptive
> Rejection Metropolis sampling which can be downloaded from the MRC-BSU
> web site. Using this you can handle more difficult cases:
> Some tuning is required for optimal performance.
> I have used this approach on a small problem, and have an increased
> appreciation of BUGS for having done so.
I don't know of any R code or packages presently available for MCMC.
However, it would be a very interesting project to design such code,
either for R or for omegahat. The concept of a function closure in R
could be quite helpful. I think Ross Ihaka and/or Robert Gentleman
wrote a paper once on how a Bayesian model would naturally be
expressed as a function closure.
It took me a long time to understand what a function closure is and
why it would be useful (my thanks for Robert Gentleman and Duncan
Temple Lang for explaining it to me - repeatedly) but for applications
like this it is definitely worth the time to learn about it.
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