Rare Event Simulation with Fully Automated Importance Splitting

Carlos Esteban Budde, P.R. D'Argenio, H. Hermanns

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Citations (Scopus)


Probabilistic model checking is a powerful tool for analysing probabilistic systems but it can only be efficiently applied to Markov models. Monte Carlo simulation provides an alternative for the generality of stochastic processes, but becomes infeasible if the value to estimate depends on the occurrence of rare events. To combat this problem, intelligent simulation strategies exist to lower the estimation variance and hence reduce the simulation time. Importance splitting is one such technique, but requires a guiding function typically defined in an ad hoc fashion by an expert in the field. We present an automatic derivation of the importance function from the model description. A prototypical tool was developed and tested on several Markov models, compared to analytically and numerically calculated results and to results of typical ad hoc importance functions, showing the feasibility and efficiency of this approach. The technique is easily adapted to general models like GSMPs.

Original languageEnglish
Title of host publicationComputer Performance Engineering
Subtitle of host publication12th European Workshop, EPEW 2015, Madrid, Spain, August 31 - September 1, 2015, Proceedings
EditorsMarta Beltran, William Knottenbelt, Jeremy Bradley
ISBN (Electronic)978-3-319-23267-6
ISBN (Print)978-3-319-23266-9
Publication statusPublished - 2015
Externally publishedYes


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