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An island particle algorithm for rare event analysis

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  • Vergé, Christelle
  • Morio, Jérôme
  • Moral, Pierre Del

Abstract

Estimating rare event probability with accuracy is of great interest for safety and reliability applications. In this paper, we focus on rare events which can be modeled by a threshold exceedance of a deterministic input–output function with random inputs. Some parameters of this function or density parameters of input random variables may be fixed by an experimenter for simplicity reasons. From a risk analysis point of view, it is not only interesting to evaluate the probability of a critical event but it is also important to determine the impact of such tuning of parameters on the realization of a critical event, because a bad estimation of these parameters can strongly modify rare event probability estimations. In the present paper, we present an example of island particle algorithm referred to as sequential Monte Carlo square (SMC2). This algorithm gives an estimate of the law of random phenomena that leads to critical events. The principles of this statistical technique are described throughout this article and its results are analysed on different realistic aerospace test cases.

Suggested Citation

  • Vergé, Christelle & Morio, Jérôme & Moral, Pierre Del, 2016. "An island particle algorithm for rare event analysis," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 63-75.
    • Handle: RePEc:eee:reensy:v:149:y:2016:i:c:p:63-75
    DOI: 10.1016/j.ress.2015.11.017
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    References listed on IDEAS

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    1. Munoz Zuniga, M. & Garnier, J. & Remy, E. & de Rocquigny, E., 2011. "Adaptive directional stratification for controlled estimation of the probability of a rare event," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1691-1712.
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    4. Fredrik Lindsten & Randal Douc & Eric Moulines, 2015. "Uniform Ergodicity of the Particle Gibbs Sampler," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 775-797, September.
    5. Richard, Jean-Francois & Zhang, Wei, 2007. "Efficient high-dimensional importance sampling," Journal of Econometrics, Elsevier, vol. 141(2), pages 1385-1411, December.
    6. Morio, Jérôme, 2011. "Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 178-183.
    7. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
    8. Jean-Francois Richard, 2007. "Efficient High-Dimensional Importance Sampling," Working Paper 321, Department of Economics, University of Pittsburgh, revised Jan 2007.
    9. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin, 2000. "Variance Reduction Techniques for Estimating Value-at-Risk," Management Science, INFORMS, vol. 46(10), pages 1349-1364, October.
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    Cited by:

    1. Gareth W. Peters & Rodrigo S. Targino & Mario V. Wüthrich, 2017. "Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks," Risks, MDPI, vol. 5(4), pages 1-51, September.
    2. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.

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