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Accelerating Simulation of Population Continuous Time Markov Chains via Automatic Model Reduction

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Original languageEnglish
Pages (from-to)20-35
Number of pages39
JournalPerformance Evaluation
Early online date11 Jan 2018
Publication statusPublished - Apr 2018


We present a novel model reduction method which can signicantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers' interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has signicantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method is demonstrated on two illustrative models.

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