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.

References in zbMATH (referenced in 35 articles , 1 standard article )

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  1. Zhang, Benjamin J.; Sahai, Tuhin; Marzouk, Youssef M.: A Koopman framework for rare event simulation in stochastic differential equations (2022)
  2. Faisant, F.; Besga, B.; Petrosyan, A.; Ciliberto, S.; Majumdar, Satya N.: Optimal mean first-passage time of a Brownian searcher with resetting in one and two dimensions: experiments, theory and numerical tests (2021)
  3. Buijsrogge, Anne; Dupuis, Paul; Snarski, Michael: Splitting algorithms for rare event simulation over long time intervals (2020)
  4. Bréhier, Charles-Edouard; Lelièvre, Tony: On a new class of score functions to estimate tail probabilities of some stochastic processes with adaptive multilevel splitting (2019)
  5. Cérou, Frédéric; Guyader, Arnaud; Rousset, Mathias: Adaptive multilevel splitting: historical perspective and recent results (2019)
  6. Evans, Martin R.; Majumdar, Satya N.: Effects of refractory period on stochastic resetting (2019)
  7. Hartmann, Carsten; Kebiri, Omar; Neureither, Lara; Richter, Lorenz: Variational approach to rare event simulation using least-squares regression (2019)
  8. Joshi, Mark S.; Zhu, Dan: The efficient computation and the sensitivity analysis of finite-time ruin probabilities and the estimation of risk-based regulatory capital (2016)
  9. Mills, Alex F.: A simple yet effective decision support policy for mass-casualty triage (2016)
  10. 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)
  11. Simonnet, Eric: Combinatorial analysis of the adaptive last particle method (2016)
  12. Gobet, E.; Liu, G.: Rare event simulation using reversible shaking transformations (2015)
  13. Borodina, A.; Morozov, E.: Accelerated consistent estimation of a high load probability in (M/G/1) and (GI/G/1) queues (2014)
  14. Cai, Yi; Dupuis, Paul: Analysis of an interacting particle method for rare event estimation (2013)
  15. Botev, Zdravko I.; Kroese, Dirk P.: Efficient Monte Carlo simulation via the generalized splitting method (2012)
  16. Krystul, Jaroslav; Le Gland, François; Lezaud, Pascal: Sampling per mode for rare event simulation in switching diffusions (2012)
  17. Blanchet, Jose; Leder, Kevin; Shi, Yixi: Analysis of a splitting estimator for rare event probabilities in Jackson networks (2011)
  18. Dean, Thomas; Dupuis, Paul: The design and analysis of a generalized RESTART/DPR algorithm for rare event simulation (2011)
  19. Bertail, P.; Clémençon, S.; Tressou, J.: Statistical analysis of a dynamic model for dietary contaminant exposure (2010)
  20. Villén-Altamirano, José: Importance functions for restart simulation of general Jackson networks (2010)

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