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Rare Event Simulation with Fully Automated Importance Splitting

  • Carlos E. Budde
  • Pedro R. D’Argenio
  • Holger Hermanns
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9272)

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.

Keywords

Rare Event Goal State Importance Sampling Importance Function Tandem Queue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Carlos E. Budde
    • 1
  • Pedro R. D’Argenio
    • 1
  • Holger Hermanns
    • 2
  1. 1.FaMAF, Universidad Nacional de Córdoba – CONICETCórdobaArgentina
  2. 2.Fakultät für Mathematik und InformatikUniversität des SaarlandesSaarbrückenGermany

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