RT Journal Article ID 532f6b8167c0004a A1 Yu, Tenchao A1 Lu, Linjun A1 Li, Jinglai T1 A WEIGHT-BOUNDED IMPORTANCE SAMPLING METHOD FOR VARIANCE REDUCTION JF International Journal for Uncertainty Quantification JO IJUQ YR 2019 FD 2019-06-27 VO 9 IS 3 SP 311 OP 319 K1 importance sampling K1 variance reduction K1 estimator variance K1 normality test AB Importance sampling (IS) is an important technique to reduce the estimation variance in Monte Carlo simulations. In many practical problems, however, the use of the 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 we 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 a rather reliable estimate when the standard IS fails, and it also outperforms the defensive IS, a popular method to prevent unbounded variance. PB Begell House LK https://www.dl.begellhouse.com/journals/52034eb04b657aea,1b58af220d28d8e5,532f6b8167c0004a.html