Proceedings of the IEEE · 1989 · 297 citations · 11 references
EngineeringComputer ScienceMathematicsProbability TheoryStochastic ModelingStochastic ProcessesDiscrete ProbabilityDiscrete-event SimulationDiscrete-event System ModelingMarkov ProcessesSemi-markov ProcessesStochastic Systems ModelingDiscrete-event ModelingDiscrete-event SystemsImportance SamplingLikelihood Ratio FrameworkSemi-markov ModellingStochastic Process Analysis
A precise mathematical framework for the study of discrete event systems is described. The idea is to define a particular type of stochastic process, called a generalized semi-Markov process (GSMP), which captures the essential dynamical structure of a discrete event system. An attempt is also made to give the flavor of the qualitative theory and numerical algorithms that can be obtained as a result of viewing discrete event systems as GSMPs. Likelihood ratio concepts for importance sampling are briefly described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Theory of ordinary differential equations
H.L. Platzer · Journal of the Franklin Institute · 1956 · 5.7K citations
Importance Sampling for Stochastic Simulations
Peter W. Glynn, Donald L. Iglehart · Management Science · 1989 · 652 citations