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

Research output: Contribution to journalArticle

Original languageEnglish
Number of pages39
JournalPerformance Evaluation
StateAccepted/In press - 2 Nov 2017

Abstract

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. Specically, given a set of predened target populations of
the modellers' interest, our method exploits the coupling coecients 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 coe-
cients on the target populations are exactly simulated. However, the remaining
population variables and transitions which have low coupling coecients can ei-
ther be removed or approximately simulated in the reduced model. The reduced
model generated by our approach has signicantly lower cost for stochastic sim-
ulation, but still retains high accuracy on the statistical properties of the target
populations. The applicability and eectiveness of our method is demonstrated
on two illustrative models.

ID: 46316078