I have been working on a general MCMC application in Python, which
implements random walk Metropolis-Hastings rather than Gibbs sampling.
This is based on the SimPy discrete event simulation module for python
(http://simpy.sourceforge.net), which is a threaded component and can
be readily extended to a parallel processing environment. The advantage
of using python is that it is cross-platform and flexible, allowing for
the integration of C/C++ and Fortran code to speed up bottlenecks in
code. My work is still in the early stages, but works well with some
sample problems, including a spatial waterfowl harvest model. I'll be
presenting a paper on this at the upcoming wildlife society meetings.
Much profiling and optimization is still required. Let me know if you
want more info, or wish to collaborate on something more grandiose.
Chris Fonnesbeck
--
Christopher J. Fonnesbeck (chris at fonnesbeck dot org)
GA Coop. Fish & Wildlife Research Unit, University of Georgia
> Here at the Upper Midwest Environmental Sciences Center we are
> evaluating
> the potential for developing a Linux-based BUGS. Our interest is in
> speeding up processing from what currently takes nearly 3 days per run
> (with final models historically averaging nearly one month of
> development
> time). We hope that by creating "LinBUGS" we might be able to
> implement a
> clustered-processing approach whereby we share the simulations across a
> number of computers. A simplistic approach would be running single
> chains
> on individual computers and then combining them post hoc. Another
approach
> might be to actually split some of the processing within chains across
> multiple computers; it is this latter approach that would harness the
power
> of clustered computing. Before we go off on some goose chase, I would
like
> to hear from the BUGS community about this issue. If you have
> previously
> considered or implemented a clustered-computing approach for BUGS,
> please
> contact me. Also, I'm rather interested in whether others feel
> processing
> time currently constrains the questions one might ask. We currently
> run
> our models on machines (1 gb RAM, 1.80 GHz processing speed) that
> aren't
> puny; each iteration takes on the order of 17 seconds. Because
convergence
> on most of the spatial models I'm working with doesn't occur until
> somewhere around 18,000 iterations, that's 2.8 days in a single run.
>
> Thanks for any discussion this might generate,
>
> Wayne E. Thogmartin, PhD
> Statistician (Biology)
> USGS Upper Midwest Environmental Sciences Center
> 2630 Fanta Reed Road
> La Crosse, WI 54603
> 608.781.6309
> [log in to unmask]
> www.umesc.usgs.gov/staff/bios/wet0.html
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