Parallel Markov Chain Monte Carlo
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Abstract
The increasing availability of multi-core and multi-processor architectures provides
new opportunities for improving the performance of many computer simulations.
Markov Chain Monte Carlo (MCMC) simulations are widely used for approximate
counting problems, Bayesian inference and as a means for estimating very highdimensional
integrals. As such MCMC has found a wide variety of applications in
fields including computational biology and physics,financial econometrics, machine
learning and image processing.
This thesis presents a number of new method for reducing the runtime of
Markov Chain Monte Carlo simulations by using SMP machines and/or clusters.
Two of the methods speculatively perform iterations in parallel, reducing the runtime
of MCMC programs whilst producing statistically identical results to conventional
sequential implementations. The other methods apply only to problem domains
that can be presented as an image, and involve using various means of dividing
the image into subimages that can be proceed with some degree of independence.
Where possible the thesis includes a theoretical analysis of the reduction in
runtime that may be achieved using our technique under perfect conditions, and
in all cases the methods are tested and compared on selection of multi-core and
multi-processor architectures. A framework is provided to allow easy construction
of MCMC application that implement these parallelisation methods