A WEIGHT-BOUNDED IMPORTANCE SAMPLING METHOD FOR VARIANCE REDUCTION



Yu, Tenchao, Lu, Linjun and Li, Jinglai ORCID: 0000-0001-7980-6901
(2019) A WEIGHT-BOUNDED IMPORTANCE SAMPLING METHOD FOR VARIANCE REDUCTION. INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 9 (3). pp. 311-319.

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Abstract

Importance sampling (IS) is an important technique to reduce the estimation variance in Monte Carlo simulations. In many practical problems, however, the use of IS method may result in unbounded variance, and thus fail to provide reliable estimates. To address the issue, we propose a method which can prevent the risk of unbounded variance; the proposed method performs the standard IS for the integral of interest in a region only in which the IS weight is bounded and use the result as an approximation to the original integral. It can be verified that the resulting estimator has a finite variance. Moreover, we also provide a normality test based method to identify the region with bounded IS weight (termed as the safe region) from the samples drawn from the standard IS distribution. With numerical examples, we demonstrate that the proposed method can yield rather reliable estimate when the standard IS fails, and it also outperforms the defensive IS, a popular method to prevent unbounded variance.

Item Type: Article
Uncontrolled Keywords: importance sampling, variance reduction, estimator variance, normality test
Depositing User: Symplectic Admin
Date Deposited: 05 Mar 2019 08:55
Last Modified: 19 Jan 2023 00:57
DOI: 10.1615/Int.J.UncertaintyQuantification.2019029511
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3033760

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