A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Importance sampling for stochastic simulations
Management Science
Likelihood ratio gradient estimation for stochastic systems
Communications of the ACM - Special issue on simulation
Discrete-time conversion for simulating finite-horizon Markov processes
SIAM Journal on Applied Mathematics
A unified view of the IPA, SF, and LR gradient estimation techniques
Management Science
Queueing Systems: Theory and Applications
Application of RPA and the harmonic gradient estimators to a priority queueing system
WSC '94 Proceedings of the 26th conference on Winter simulation
Two approaches for estimating the gradient in functional form
WSC '93 Proceedings of the 25th conference on Winter simulation
The surrogate estimation approach for sensitivity analysis in queueing networks
WSC '93 Proceedings of the 25th conference on Winter simulation
An overview of derivative estimation
WSC '91 Proceedings of the 23rd conference on Winter simulation
Comparing alternative methods for derivative estimation when IPA does not apply directly
WSC '91 Proceedings of the 23rd conference on Winter simulation
Estimating small cell-loss ratios in ATM switches via importance sampling
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Customer-Oriented Finite Perturbation Analysis for QueueingNetworks
Discrete Event Dynamic Systems
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We consider a class of stochastic models for whichthe performance measure is defined as a mathematical expectationthat depends on a parameter \theta, say \alpha(\theta),and we are interested in constructing estimators of \alphain functional form (i.e., entire functions of \theta),which can be computed from a single simulation experiment. Wefocus on the case where \theta is a continuous parameter,and also consider estimation of the derivative \alpha‘(\theta).One approach for doing that, when \theta is a parameterof the probability law that governs the system, is based on theuse of likelihood ratios and score functions. In this paper,we study a different approach, called split-and-merge, for thecase where \theta is a threshold parameter. Thisapproach can be viewed as a practical way of running parallelsimulations at an infinite number of values of \theta,with common random numbers. We give several examples showinghow different kinds of parameters such as the arrival rate ina queue, the probability that an arriving customer be of a giventype, a scale parameter of a service time distribution, and soon, can be turned into threshold parameters. We also discussimplementation issues.