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DOE OSTI · 1457397

Moderate deviations-based importance sampling for stochastic recursive equations

Dupuis, Paul·Johnson, Dane
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Abstract

Subsolutions to the Hamilton–Jacobi–Bellman equation associated with a moderate deviations approximation are used to design importance sampling changes of measure for stochastic recursive equations. Analogous to what has been done for large deviations subsolution-based importance sampling, these schemes are shown to be asymptotically optimal under the moderate deviations scaling. Here, we present various implementations and numerical results to contrast their performance, and also discuss the circumstances under which a moderate deviation scaling might be appropriate.

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97 MATHEMATICS AND COMPUTINGaccelerated Monte Carloimportance samplingModerate deviationsrare event

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Dupuis, Paul, Johnson, Dane. 2017-11-17. Moderate deviations-based importance sampling for stochastic recursive equations. https://doi.org/10.1017/apr.2017.31

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