NDLI: Approximate Zero-Variance Importance Sampling for Static Network Reliability Estimation

Content Provider	IEEE Xplore Digital Library
Author	L'Ecuyer, P. Rubino, G. Saggadi, S. Tuffin, B.
Copyright Year	1963
Abstract	We propose a new Monte Carlo method, based on dynamic importance sampling, to estimate the probability that a given set of nodes is connected in a graph (or network) where each link is failed with a given probability. The method generates the link states one by one, using a sampling strategy that approximates an ideal zero-variance importance sampling scheme. The approximation is based on minimal cuts in subgraphs. In an asymptotic rare-event regime where failure probability becomes very small, we prove that the relative error of our estimator remains bounded, and even converges to 0 under additional conditions, when the unreliability of individual links converges to 0. The empirical performance of the new sampling scheme is illustrated by examples.
Sponsorship	IEEE Reliability Society
Starting Page	590
Ending Page	604
Page Count	15
File Size	508830
File Format	PDF
ISSN	00189529
Volume Number	60
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Approximation methods Markov processes Telecommunication network reliability Robustness Estimation variance reduction network reliability
Content Type	Text
Resource Type	Article
Subject	Safety, Risk, Reliability and Quality Electrical and Electronic Engineering

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