DocumentCode :
2165362
Title :
Adaptive control variates
Author :
Kim, Sujin ; Henderson, Shane G.
Author_Institution :
Sch. of Oper. Res. & Ind. Eng., Cornell Univ., Ithaca, NY, USA
Volume :
1
fYear :
2004
fDate :
5-8 Dec. 2004
Lastpage :
629
Abstract :
Adaptive Monte Carlo methods are specialized Monte Carlo simulation techniques where the methods are adaptively tuned as the simulation progresses. The primary focus of such techniques has been in adaptively tuning importance sampling distributions to reduce the variance of an estimator. We instead focus on adaptive control variate schemes, developing asymptotic theory for the performance of two adaptive control variate estimators. The first estimator is based on a stochastic approximation scheme for identifying the optimal choice of control variate. It is easily implemented, but its performance is sensitive to certain tuning parameters, the selection of which is nontrivial. The second estimator uses a sample average approximation approach. It has the advantage that it does not require any tuning parameters, but it can be computationally expensive and requires the availability of nonlinear optimization software.
Keywords :
Markov processes; adaptive control; estimation theory; importance sampling; optimal control; optimisation; parameter estimation; Monte Carlo simulation technique; adaptive Monte Carlo method; adaptive control variate estimator; asymptotic theory; importance sampling distribution; nonlinear optimization software; sample average approximation approach; stochastic approximation scheme; Adaptive control; Analytical models; Availability; Industrial engineering; Monte Carlo methods; Operations research; Optimal control; Parameter estimation; Random variables; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Simulation Conference, 2004. Proceedings of the 2004 Winter
Print_ISBN :
0-7803-8786-4
Type :
conf
DOI :
10.1109/WSC.2004.1371369
Filename :
1371369
Link To Document :
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