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October 2009 Robust adaptive importance sampling for normal random vectors
Benjamin Jourdain, Jérôme Lelong
Ann. Appl. Probab. 19(5): 1687-1718 (October 2009). DOI: 10.1214/09-AAP595


Adaptive Monte Carlo methods are very efficient techniques designed to tune simulation estimators on-line. In this work, we present an alternative to stochastic approximation to tune the optimal change of measure in the context of importance sampling for normal random vectors. Unlike stochastic approximation, which requires very fine tuning in practice, we propose to use sample average approximation and deterministic optimization techniques to devise a robust and fully automatic variance reduction methodology. The same samples are used in the sample optimization of the importance sampling parameter and in the Monte Carlo computation of the expectation of interest with the optimal measure computed in the previous step. We prove that this highly dependent Monte Carlo estimator is convergent and satisfies a central limit theorem with the optimal limiting variance. Numerical experiments confirm the performance of this estimator: in comparison with the crude Monte Carlo method, the computation time needed to achieve a given precision is divided by a factor between 3 and 15.


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Benjamin Jourdain. Jérôme Lelong. "Robust adaptive importance sampling for normal random vectors." Ann. Appl. Probab. 19 (5) 1687 - 1718, October 2009.


Published: October 2009
First available in Project Euclid: 16 October 2009

zbMATH: 1202.62106
MathSciNet: MR2569805
Digital Object Identifier: 10.1214/09-AAP595

Primary: 60F05 , 62L20 , 65C05 , 90C15

Keywords: Adaptive importance sampling , central limit theorem , sample averaging

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.19 • No. 5 • October 2009
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