Importance Sampling methods allow to substantially reduce the number of trials in estimation of the rare failure probability or other stochastic metrics. These methods can be viewed as a rigorous generalization of quantitative “torture” or “stress” methods where the process is artificially modified to increase the probability of failure, and the failure probability estimations obtained for such modified process are extrapolated to the original process with rare failures. Applications of Importance Sampling methods are presented and demonstrated on computationally efficient estimations of via failure probability and via LCDU. The accuracy of the Importance Sampling LCDU estimates is verified by comparison with experimental results. Applications of Importance Sampling methods to experimental measurements are discussed.
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