On performance potentials and conditional Monte Carlo for gradient estimationfor Markov chains

X.-R. Cao, M.C. Fu and J.-Q. Hu

Annals of Operations Research, 1999, vol. 87, issue 0, 263-272

Abstract: We consider the problem of sample path‐based gradient estimation for long‐run (steady‐state) performance measures defined on discrete‐time Markov chains. We show how two estimators ‐ one derived using the likelihood ratio method with conditional Monte Carlo and splitting, and the other derived using performance potentials and perturbation analysis ‐are related. In particular, one can be expressed as the conditional expectation of a suitably weighted average of the other. This demonstrates yet another connection between the two gradient estimation techniques of perturbation analysis and the likelihood ratio method. Copyright Kluwer Academic Publishers 1999

Date: 1999
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DOI: 10.1023/A:1018985019884

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