An Adaptive Zero-Variance Importance Sampling Approximation for Static Network Dependability Evaluation
1 DIRO, Université de Montréal , C.P. 6128, Succ. Centre-Ville, Montréal, H3C 3J7, Canada
2 INRIA Rennes – Bretagne Atlantique, Campus Universitaire de Beaulieu, 35042 Rennes Cedex, France
We propose an adaptive parameterized method to approximate the zero-variance change of measure for the evaluation of static network reliability models, with links subject to failures. The method uses two rough approximations of the unreliability function, conditional on the states of any subset of links being fixed. One of these approximation, based on mincuts, under-estimates the true unknown unreliability, whereas the other one, based on minpaths, over-estimates it. Our proposed change of measure takes a convex linear combination of the two, estimates the optimal (graph-dependent) coefficient in this combination from pilot runs, and uses the resulting conditional unreliability approximation at each step of a dynamic importance sampling algorithm. This new scheme is more general and more flexible than a previously-proposed zero-variance approximation scheme, based on mincuts only.
Key words: Monte Carlo / importance sampling / rare events / dependability / network reliability
© Owned by the authors, published by EDP Sciences, 2014