Convergence measure and some parallel aspects of Markov-chain Monte Carlo algorithms

Maurits J. Malfait; Dirk Roose; Dirk Vandermeulen

doi:10.1117/12.162042

29 October 1993 Convergence measure and some parallel aspects of Markov-chain Monte Carlo algorithms

Maurits J. Malfait, Dirk Roose, Dirk Vandermeulen

Proceedings Volume 2032, Neural and Stochastic Methods in Image and Signal Processing II; (1993) https://doi.org/10.1117/12.162042
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

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.

Citation Download Citation

Maurits J. Malfait, Dirk Roose, and Dirk Vandermeulen "Convergence measure and some parallel aspects of Markov-chain Monte Carlo algorithms", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); https://doi.org/10.1117/12.162042

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