Abstract
We discuss the simulation ofM replications of a uniformizable Markov chain simultaneously and in parallel (the so-called parallel replicated approach). Distributed implementation on a number of processors and parallel SIMD implementation on massively parallel computers are described. We investigate various ways of inducing correlation across replications in order to reduce the variance of estimators obtained from theM replications. In particular, we consider the adaptation of Stratified Sampling, Latin Hypercube Sampling, and Rotation Sampling to our setting. These algorithms can be used in conjunction with the Standard Clock simulation of uniformized chains at distinct parameter values and can potentially sharpen multiple comparisons of systems in that setting. Our investigation is primarily motivated by this consideration.
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Streltsov, S., Vakili, P. Variance reduction algorithms for parallel replicated simulation of uniformized Markov chains. Discrete Event Dyn Syst 6, 159–180 (1996). https://doi.org/10.1007/BF01797237
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DOI: https://doi.org/10.1007/BF01797237