A comparison of cross-entropy and variance minimization strategies

Joshua C.C. Chan, Peter W. Glynn, Dirk P. Kroese*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    21 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)183-194
    Number of pages12
    JournalJournal of Applied Probability
    Volume48A
    DOIs
    Publication statusPublished - Aug 2011

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