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Most Downloaded Articles
- Simulation of binary random fields with Gaussian numerical models by Prigarin, Sergei M./ Martin, Andreas and Winkler, Gerhard
- A Green's function Monte Carlo algorithm for the Helmholtz equation subject to Neumann and mixed boundary conditions: Validation with an 1D benchmark problem by Chatterjee, Kausik and Anantapadmanabhan, Akshay
- On convergence of semi-statistical and projection-statistical methods for integral equations by Ivanov, Vladimir M. and Korenevski, Maxim L.
- The generalized van der Corput sequence and its application to numerical integration by Fujita, Takahiko/ Ito, Shunji and Ninomiya, Syoiti
Adaptive Monte Carlo Variance Reduction with Two-time-scale Stochastic Approximation
1Center for the Study of Finance and Insurance, Osaka University, Toyonaka, 560-8531, Japan.
2Email: reiichiro email@example.com
Citation Information: Monte Carlo Methods and Applications mcma. Volume 13, Issue 3, Pages 197–217, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: 10.1515/mcma.2007.010, August 2007
- Published Online:
Combined control variates and importance sampling variance reduction and its two-fold optimality are investigated. Two-time-scale stochastic approximation algorithm is applied in parameter search for the combination and almost sure convergence of the algorithm to the unique optimum is proved. The parameter search procedure is further incorporated into adaptive Monte Carlo simulation, and its law of large numbers and central limit theorem are proved to hold. An numerical example is provided to illustrate the effectiveness of the method.