A New Technique for MTTF Estimation in Highly Reliable Markovian Systems
Copyright policy )A New Technique for MTTF Estimation in Highly Reliable Markovian Systems
A new method for the importance sampling simulation of highly reliable Markovian systems is presented. The method has the bounded relative error for balanced and under an additional assumption, unbalanced systems. It has shown very high efficiency in the estimation of the mean time to failure (MTTF) of a system, which is a measure of great importance. Using this method one may have orders of magnitude in variance reduction as compared to the standard simulation and other already existing methods. Examples are presented to show the efficiency of the method.
numerical examples, highly reliable Markovian systems, variance reduction, Sampling theory, sample surveys, Analysis of variance and covariance (ANOVA), importance sampling simulation, bounded relative error, Probabilistic methods, stochastic differential equations
numerical examples, highly reliable Markovian systems, variance reduction, Sampling theory, sample surveys, Analysis of variance and covariance (ANOVA), importance sampling simulation, bounded relative error, Probabilistic methods, stochastic differential equations
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