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Moderate deviations-based importance sampling for stochastic recursive equations

Journal Article · · Advances in Applied Probability
DOI:https://doi.org/10.1017/apr.2017.31· OSTI ID:1457397
 [1];  [2]
  1. Brown Univ., Providence, RI (United States); Brown University
  2. Univ. of North Carolina, Chapel Hill, NC (United States)
+ Show Author Affiliations

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.

Research Organization:
Brown Univ., Providence, RI (United States)
Sponsoring Organization:
USDOE Office of Science (SC); National Science Foundation (NSF); Army Research Office (ARO); Defense Advanced Research Projects Agency (DARPA)
DOE Contract Number:
SC0010539
OSTI ID:
1457397
Journal Information:
Advances in Applied Probability, Journal Name: Advances in Applied Probability Journal Issue: 04 Vol. 49; ISSN 0001-8678; ISSN AAPBBD
Publisher:
Cambridge University Press
Country of Publication:
United States
Language:
English

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Related Subjects

97 MATHEMATICS AND COMPUTING
Moderate deviations
accelerated Monte Carlo
importance sampling
rare event