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
Sharpening comparisons via gaussian copulas and semidefinite programming
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Adaptive Monte Carlo methods are simulation efficiency improvement techniques designed to adaptively tune simulation estimators. Most of the work on adaptive Monte Carlo methods has been devoted to adaptively tuning importance sampling schemes. We instead focus on adaptive methods based on control variate schemes. We introduce two adaptive control variate methods, and develop their asymptotic properties. The first method uses stochastic approximation to adaptively tune control variate estimators. It is easy to implement, but it requires some nontrivial tuning of parameters. The second method is based on sample average approximation. Tuning is no longer required, but it can be computationally expensive. Numerical results for the pricing of barrier options are presented to demonstrate the methods.