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Proceedings Paper

Convergence measure and some parallel aspects of Markov-chain Monte Carlo algorithms
Author(s): Maurits J. Malfait; Dirk Roose; Dirk Vandermeulen
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Paper Abstract

We examine methods to assess the convergence of Markov chain Monte Carlo (MCMC) algorithms and to accelerate their execution via parallel computing. We propose a convergence measure based on the deviations between simultaneously running MCMC algorithms. We also examine the acceleration of MCMC algorithms when independent parallel sampler are used and report on some experiments with coupled samplers. As applications we use small Ising model simulations and a larger medical image processing algorithm.

Paper Details

Date Published: 29 October 1993
PDF: 12 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162042
Show Author Affiliations
Maurits J. Malfait, Katholieke Univ. Leuven (Belgium)
Dirk Roose, Katholieke Univ. Leuven (Belgium)
Dirk Vandermeulen, Katholieke Univ. Leuven (Belgium)

Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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