An adaptive zero-variance importance sampling approximation for static network dependability evaluation

• Published in: Computers & operations research Vol. 45; pp. 51 - 59
• Main Authors: Tuffin, Bruno, Saggadi, Samira, L'Ecuyer, Pierre
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.05.2014; Elsevier; Pergamon Press Inc
• Subjects: Applied sciences; Approximation; Asymptotic methods; Asymptotic properties; Computer Science; Exact sciences and technology; Failure; Importance sampling; Links; Mathematical analysis; Mathematical models; Mathematics; Monte Carlo simulation; Network flow problem; Networks; Operational research and scientific management; Operational research. Management science; Operations Research; Optimization algorithms; Probability and statistics; Reliability; Reliability theory. Replacement problems; Sampling techniques; Sampling theory, sample surveys; Sciences and techniques of general use; Statistics; Studies; Reliability; Monte Carlo simulation; Pilot; Monte Carlo method; Dependability; Roughness; Approximation algorithm; Failures; Modeling; Adaptive method; Network reliability; Linear combination; Static model; Variance reduction; Asymptotic approximation; Interconnected system; Importance sampling

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 l...