Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
The score function approach for sensitivity analysis of computer simulation models
Mathematics and Computers in Simulation
Monte Carlo optimization, simulation, and sensitivity of queueing networks
Monte Carlo optimization, simulation, and sensitivity of queueing networks
Convergence properties of infinitesimal perturbation analysis
Management Science
Operations Research
Importance sampling for stochastic simulations
Management Science
Sensitivity analysis via likelihood ratios
WSC '86 Proceedings of the 18th conference on Winter simulation
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Principles of Discrete Event Simulation
Principles of Discrete Event Simulation
Stochastic approximation for Monte Carlo optimization (1986)
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
“What-if” analysis in computer simulation models: A comparative survey with some extensions
Mathematical and Computer Modelling: An International Journal
Generalized estimates for performance sensitivities of stochastic systems
Mathematical and Computer Modelling: An International Journal
Real-time multiserver and multichannel systems with shortage of maintenance crews
Mathematical and Computer Modelling: An International Journal
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We discuss some known and some new results on the score function (SF) approach for simulation analysis. We show that while simulating a single sample path from the underlying system or from an associated system and applying the Radon-Nikodym measure one can: estimate the performance sensitivities (gradient, Hessian etc.) of the underlying system with respect to some parameter (vector of parameters); extrapolate the performance measure for different values of the parameters; evaluate the performance measures of queuing models working in heavy traffic by simulating an associated (auxiliary) queuing model working in light (lighter) traffic; evaluate the performance measures of stochastic models while simulating random vectors (say, by the inverse transform method) from an auxiliary probability density function rather than from the original one (say by the acceptance-rejection method). Applications of the SF approach to a broad variety of stochastic models are given.