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

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...

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Bibliographic Details
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
ISSN:	0305-0548, 1873-765X, 0305-0548
Online Access:	Get full text
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