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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
Early online date11 Jan 2018
StateE-pub ahead of print - 11 Jan 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 coeffi-
cients on the target populations are exactly simulated. However, the remaining
population variables and transitions which have low coupling coefficients 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 effectiveness of our method is demonstrated
on two illustrative models.

ID: 46316078