Parallel Computing for Statistics
30 May 2008
13:30 - 16:00
Errol Street
ROB CROUCHLEY (Lancaster University)
Some Grid Enabled Tools for Statistical Research
Tools for computationally demanding statistical research are becoming
available as part of commercial systems, e.g. SAS grid computing and
Stata MP. However, these systems can be of limited use on a public grid,
e.g. Stata MP can't access multiple data sets and neither system
provides access to their source code. Furthermore, there are no plans to
install them on the UK National Grid Service (NGS) because of
cost/licensing issues. The merits of using R as a framework for such
computation are described.
It is intended to demonstrate several examples of statistical computing
using R in conjunction with the multiR and sabreRgrid packages and
distributed computing resources provided by the National Grid Service
(NGS).
DARREN WILKINSON (Newcastle University)
Parallel Bayesian computation
The use of Bayesian inference for the analysis of complex statistical
models has increased dramatically in recent years. There are a range
of techniques available for carrying out Bayesian inference, but the
lack of analytic tractability for the vast majority of models of
interest means that most of the techniques are numeric, and many are
computationally demanding. Indeed, for high dimensional non-linear
models, the only practical methods for analysis are based on Markov
chain Monte Carlo (MCMC) techniques, and these are notoriously compute
intensive, with some analyses requiring weeks of CPU time on powerful
computers. It is clear therefore that the use of parallel computing
technology in the context of Bayesian computation is of great interest
to many who analyse complex models using Bayesian techniques. This talk
will review the problems and possibilities associated with parallelising
MCMC algorithms and running large MCMC codes on multi-processor
computers, clusters and GRIDs.
Meeting website: http://www.rss.org.uk/main.asp?page=1321&event=529
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