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1 results

  • A comparison of cross-entropy and variance minimization strategies

  • Part of
  • Joshua C. C. Chan, Peter W. Glynn, Dirk P. Kroese
  • Journal:
    Journal of Applied Probability / Volume 48 / Issue A / August 2011
    Published online by Cambridge University Press:
    14 July 2016, pp. 183-194
    Print publication:
    August 2011
    • Article
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  • The variance minimization (VM) and cross-entropy (CE) methods are two versatile adaptive importance sampling procedures that have been successfully applied to a wide variety of difficult rare-event estimation problems. We compare these two methods via various examples where the optimal VM and CE importance densities can be obtained analytically. We find that in the cases studied both VM and CE methods prescribe the same importance sampling parameters, suggesting that the criterion of minimizing the CE distance is very close, if not asymptotically identical, to minimizing the variance of the associated importance sampling estimator.