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
Likelihood ratio gradient estimation for stochastic systems
Communications of the ACM - Special issue on simulation
A unified view of the IPA, SF, and LR gradient estimation techniques
Management Science
Stochastic approximation for Monte Carlo optimization
WSC '86 Proceedings of the 18th conference on Winter simulation
Likelilood ratio gradient estimation: an overview
WSC '87 Proceedings of the 19th conference on Winter simulation
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
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Simulation is an essential tool for performance evaluation of many practical systems where planners typically want to know how the system will perform under various parameter settings. Since large-scale simulation may require great amount of computer time and storage, appropriate statistical analysis can become quite costly. In this paper, we develop an interpolation technique as an effective tool for estimating system respones to parametric perturbations in simulation. We also analyze the usefulness of the continuous-time Markov chains frame-work to find the likelihood ratio (Radon- Nikodym derivative) for Markovian single server queueing models. We provide numerical experiments that demonstrate how the interpolation technique significantly outperform the likelihood ratio performance extrapolation technique in the context of the Markovian queueing models in transient analysis.