Performance Evaluation and Policy Selection in Multiclass Networks
Discrete Event Dynamic Systems
In Search of Sensitivity in Network Optimization
Queueing Systems: Theory and Applications
WSC '04 Proceedings of the 36th conference on Winter simulation
Function-approximation-based perfect control variates for pricing American options
WSC '05 Proceedings of the 37th conference on Winter simulation
Exponential bounds and stopping rules for MCMC and general Markov chains
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
Proceedings of the 38th conference on Winter simulation
Exploiting regenerative structure to estimate finite time averages via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Non-linear control variates for regenerative steady-state simulation
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Approximate zero-variance simulation
Proceedings of the 40th Conference on Winter Simulation
Coupling control variates for Markov chain Monte Carlo
Journal of Computational Physics
Zero-Variance Importance Sampling Estimators for Markov Process Expectations
Mathematics of Operations Research
Zero variance Markov chain Monte Carlo for Bayesian estimators
Statistics and Computing
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"Knowledge of either analytical or numerical approximations should enable more efficient simulation estimators to be constructed." This principle seems intuitively plausible and certainly attractive, yet no completely satisfactory general methodology has been developed to exploit it. The authors present a new approach for obtaining variance reduction in Markov process simulation that is applicable to a vast array of different performance measures. The approach relies on the construction of a martingale that is then used as an internal control variate.