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Statistics and Computing

, Volume 22, Issue 3, pp 795–808 | Cite as

Sequential Monte Carlo for rare event estimation

  • F. CérouEmail author
  • P. Del Moral
  • T. Furon
  • A. Guyader
Article

Abstract

This paper discusses a novel strategy for simulating rare events and an associated Monte Carlo estimation of tail probabilities. Our method uses a system of interacting particles and exploits a Feynman-Kac representation of that system to analyze their fluctuations. Our precise analysis of the variance of a standard multilevel splitting algorithm reveals an opportunity for improvement. This leads to a novel method that relies on adaptive levels and produces, in the limit of an idealized version of the algorithm, estimates with optimal variance. The motivation for this theoretical work comes from problems occurring in watermarking and fingerprinting of digital contents, which represents a new field of applications of rare event simulation techniques. Some numerical results show performance close to the idealized version of our technique for these practical applications.

Keywords

Rare event Sequential importance sampling Feynman-Kac formula Metropolis-Hastings Fingerprinting Watermarking 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • F. Cérou
    • 1
    Email author
  • P. Del Moral
    • 2
  • T. Furon
    • 1
  • A. Guyader
    • 3
  1. 1.INRIA Rennes - Bretagne AtlantiqueRennes CedexFrance
  2. 2.INRIA Bordeaux Sud-Ouest & Institut de Mathématiques de BordeauxUniversité Bordeaux 1Talence CedexFrance
  3. 3.Equipe de StatistiqueUniversité de Haute BretagneRennes CedexFrance

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