RESTART: A method for accelerating rare event simulations This paper presents a method for accelerating simulations to estimate the probability of occurrence of rare events. The method, called RESTART (REpetitive Simulation Trials After Reaching Thresholds), is quite general and has a straightforward application, allowing dramatic reductions of the simulation time for an equal confidence of the results. The paper proves the efficiency of the method and shows an application example.

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  1. Mills, Alex F.: A simple yet effective decision support policy for mass-casualty triage (2016)
  2. Rolland, Joran; Bouchet, Freddy; Simonnet, Eric: Computing transition rates for the 1-D stochastic Ginzburg-Landau-Allen-Cahn equation for finite-amplitude noise with a rare event algorithm (2016)
  3. Simonnet, Eric: Combinatorial analysis of the adaptive last particle method (2016)
  4. Gobet, E.; Liu, G.: Rare event simulation using reversible shaking transformations (2015)
  5. Borodina, A.; Morozov, E.: Accelerated consistent estimation of a high load probability in $M/G/1$ and $GI/G/1$ queues (2014)
  6. Cai, Yi; Dupuis, Paul: Analysis of an interacting particle method for rare event estimation (2013)
  7. Botev, Zdravko I.; Kroese, Dirk P.: Efficient Monte Carlo simulation via the generalized splitting method (2012)
  8. Krystul, Jaroslav; Le Gland, François; Lezaud, Pascal: Sampling per mode for rare event simulation in switching diffusions (2012)
  9. Blanchet, Jose; Leder, Kevin; Shi, Yixi: Analysis of a splitting estimator for rare event probabilities in Jackson networks (2011)
  10. Dean, Thomas; Dupuis, Paul: The design and analysis of a generalized RESTART/DPR algorithm for rare event simulation (2011)
  11. Villén-Altamirano, José: Importance functions for restart simulation of general Jackson networks (2010)
  12. Lagnoux-Renaudie, Agnès: A two-step branching splitting model under cost constraint for rare event analysis (2009)
  13. Botev, Zdravko I.; Kroese, Dirk P.: An efficient algorithm for rare-event probability estimation, combinatorial optimization, and counting (2008)
  14. Lagnoux-Renaudie, Agnès: Effective branching splitting method under cost constraint (2008)
  15. Cérou, Frédéric; Guyader, Arnaud: Adaptive multilevel splitting for rare event analysis (2007)
  16. Villén-Altamirano, José: Rare event restart simulation of two-stage networks (2007)
  17. del Moral, Pierre; Lezaud, Pascal: Branching and interacting particle interpretations of rare event probabilities (2006)
  18. Figueiredo, Daniel R.; Liu, Benyuan; Guo, Yang; Kurose, Jim; Towsley, Don: On the efficiency of fluid simulation of networks (2006)
  19. de Boer, Pieter-Tjerk: Rare-event simulation of non-Markovian queueing networks using a state-dependent change of measure determined using cross-entropy (2005)
  20. Rubinstein, Reuven Y.; Kroese, Dirk P.: The cross-entropy method. A unified approach to combinatorial optimization, Monte-Carlo simulation and machine learning. (2004)

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