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A Unified View of the IPA, SF, and LR Gradient Estimation Techniques

Author

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  • Pierre L'Ecuyer

    (Département d'informatique, Université Laval, Ste-Foy, Québec, Canada G1K 7P4)

Abstract

We study the links between the likelihood-ratio (LR) gradient-estimation technique (sometimes called the score-function (SF) method), and infinitesimal perturbation analysis (IPA). We show how IPA can be viewed as a (degenerate) special case of the LR and SF techniques by selecting an appropriate representation of the underlying sample space for a given simulation experiment. We also show how different definitions of the sample space yield different variants of the LR method, some of them mixing IPA with more straightforward LR. We illustrate this by many examples. We also give sufficient conditions under which the gradient estimators are unbiased.

Suggested Citation

  • Pierre L'Ecuyer, 1990. "A Unified View of the IPA, SF, and LR Gradient Estimation Techniques," Management Science, INFORMS, vol. 36(11), pages 1364-1383, November.
    • Handle: RePEc:inm:ormnsc:v:36:y:1990:i:11:p:1364-1383
    DOI: 10.1287/mnsc.36.11.1364
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    Cited by:

    1. Li, Jinghui & Mosleh, Ali & Kang, Rui, 2011. "Likelihood ratio gradient estimation for dynamic reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1667-1679.
    2. Haurie, A., 1995. "Time scale decomposition in production planning for unreliable flexible manufacturing systems," European Journal of Operational Research, Elsevier, vol. 82(2), pages 339-358, April.
    3. Jank, Wolfgang, 2005. "Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 685-701, April.

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