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Optimisation of interacting particle systems for rare event estimation

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  • Morio, Jérôme
  • Jacquemart, Damien
  • Balesdent, Mathieu
  • Marzat, Julien

Abstract

The interacting particle system (IPS) is a recent probabilistic model proposed to estimate rare event probabilities for Markov chains. The principle of IPS is to apply alternatively selection and mutation stages to a set of initial particles in order to estimate probabilities or quantiles more accurately than with usual estimation techniques. The practical issue of IPS is the tuning of a parameter in the selection stage. Kriging-based optimisation strategy with a low simulation cost is thus proposed in order to minimise the probability estimate relative error. The efficiency of the proposed strategy is demonstrated on different test cases.

Suggested Citation

  • Morio, Jérôme & Jacquemart, Damien & Balesdent, Mathieu & Marzat, Julien, 2013. "Optimisation of interacting particle systems for rare event estimation," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 117-128.
  • Handle: RePEc:eee:csdana:v:66:y:2013:i:c:p:117-128
    DOI: 10.1016/j.csda.2013.03.025
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    References listed on IDEAS

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    1. Carvalho, Carlos M. & Lopes, Hedibert F., 2007. "Simulation-based sequential analysis of Markov switching stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4526-4542, May.
    2. Wolters, Mark A., 2012. "A particle swarm algorithm with broad applicability in shape-constrained estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2965-2975.
    3. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
    4. René Carmona & Jean-Pierre Fouque & Douglas Vestal, 2009. "Interacting particle systems for the computation of rare credit portfolio losses," Finance and Stochastics, Springer, vol. 13(4), pages 613-633, September.
    5. Banerjee, Sudipto & Gelfand, Alan E., 2006. "Bayesian Wombling: Curvilinear Gradient Assessment Under Spatial Process Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1487-1501, December.
    6. Voutilainen, Arto & Kaipio, Jari P., 2005. "Sequential Monte Carlo estimation of aerosol size distributions," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 887-908, April.
    7. Jasra, Ajay & Doucet, Arnaud & Stephens, David A. & Holmes, Christopher C., 2008. "Interacting sequential Monte Carlo samplers for trans-dimensional simulation," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1765-1791, January.
    8. Gen, Mitsuo & Yun, YoungSu, 2006. "Soft computing approach for reliability optimization: State-of-the-art survey," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1008-1026.
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