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This paper surveys efficient techniques for estimating, via simulation, the probabilities of certain rare events in queueing and reliability models. The rare events of interest are long waiting times or buffer overflows in queueing systems, and system failure events in reliability models of highly dependable computing systems. The general approach to(More)
This paper discusses a method for placing confidence limits on the steady state mean of an output sequence generated by a discrete event simulation. An estimate of the variance is obtained by estimating the spectral density at zero frequency. This estimation is accomplished through a regression analysis of the logarithm of the averaged periodogram. By(More)
This paper develops a variance reduction technique for Monte Carlo simulations of path-dependent options driven by high-dimensional Gaussian vectors. The method combines importance sampling based on a change of drift with stratified sampling along a small number of key dimensions. The change of drift is selected through a large deviations analysis and is(More)
1 Motivation The estimation of rare event probabilities poses some of the of the most diicult computational challenges for Monte Carlo simulation and, at the same time, some of the greatest opportunities for eeciency improvement through the use of variance reduction techniques. Current i n terest in rare events stems primarily from developments in computer(More)
This paper considers the application of importance sampling to simulations of highly available systems. By regenerative process theory, steady state performance measures of a Markov chain take the form of a ratio. Analysis of a simple three state Birth and Death process shows that the optimal (zero variance) importance sampling distributions for the(More)