Speed-up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction

Cheng Feng, Jane Hillston

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a novel statistical model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. This is achieved by identifying and removing agent types and transitions from the simulation which have only minor impact on the evolution of population dynamics of target agent types specified by the modeller. The error induced on the target agent types can be measured by a normalized coupling coefficient, which is calculated by an error propagation method over a directed relation graph for the PCTMC, using a limited number of simulation runs of the full model. Those agent types and transitions with minor impact are safely removed without incurring a significant error on the simulation result. To demonstrate the approach, we show the usefulness of our statistical reduction method by applying it to 50 randomly generated PCTMC models corresponding to different city bike-sharing scenarios.
Original languageEnglish
Title of host publicationComputer Performance Engineering
Subtitle of host publication12th European Workshop, EPEW 2015, Madrid, Spain, August 31 - September 1, 2015, Proceedings
PublisherSpringer Berlin Heidelberg
Number of pages15
ISBN (Electronic)978-3-319-23267-6
ISBN (Print)978-3-319-23266-9
Publication statusPublished - 22 Aug 2015
EventEuropean Performance Engineering Workshop - Madrid, Spain
Duration: 31 Aug 20151 Sep 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


ConferenceEuropean Performance Engineering Workshop


  • stochastic simulation
  • model reduction
  • stochastic process algebra


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