Abstract
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.
Supported by ANPCyT project PICT-2012-1823, SeCyT-UNC program 05/BP12 and their related projects, EU 7FP grant agreements 295261 (MEALS) and 318490 (SENSATION), by the DFG as part of SFB/TR 14 AVACS, by the CAS/SAFEA International Partnership Program for Creative Research Teams, and by the CDZ project CAP (GZ 1023)
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Budde, C.E., D’Argenio, P.R., Hermanns, H. (2015). Rare Event Simulation with Fully Automated Importance Splitting. In: Beltrán, M., Knottenbelt, W., Bradley, J. (eds) Computer Performance Engineering. EPEW 2015. Lecture Notes in Computer Science(), vol 9272. Springer, Cham. https://doi.org/10.1007/978-3-319-23267-6_18
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DOI: https://doi.org/10.1007/978-3-319-23267-6_18
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