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Main Author: Gao, Zhiwei
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2512.17766
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author Gao, Zhiwei
author_facet Gao, Zhiwei
contents In this manuscript, we investigate importance sampling methods for rare-event simulation in diffusion processes. We show, from a large-deviation perspective, that the resulting importance sampling estimator is log-efficient. This connection is established via a stochastic optimal control formulation, and the associated Hamilton--Jacobi--Bellman (HJB) equation is derived using dynamic programming. To approximate the optimal control, we adopt a spectral parameterization and employ the cross-entropy method to estimate the parameters by solving a least-squares problem. Finally, we present a numerical example to validate the effectiveness of the cross-entropy approach and the efficiency of the resulting importance sampling estimator.
format Preprint
id arxiv_https___arxiv_org_abs_2512_17766
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Short Report on Importance Sampling for Rare Event Simulation in Diffusions
Gao, Zhiwei
Numerical Analysis
Probability
In this manuscript, we investigate importance sampling methods for rare-event simulation in diffusion processes. We show, from a large-deviation perspective, that the resulting importance sampling estimator is log-efficient. This connection is established via a stochastic optimal control formulation, and the associated Hamilton--Jacobi--Bellman (HJB) equation is derived using dynamic programming. To approximate the optimal control, we adopt a spectral parameterization and employ the cross-entropy method to estimate the parameters by solving a least-squares problem. Finally, we present a numerical example to validate the effectiveness of the cross-entropy approach and the efficiency of the resulting importance sampling estimator.
title A Short Report on Importance Sampling for Rare Event Simulation in Diffusions
topic Numerical Analysis
Probability
url https://arxiv.org/abs/2512.17766