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Journal Articles ESAIM: Probability and Statistics Year : 2007

Minimum variance importance sampling via Population Monte Carlo

A. Guillin
  • Function : Author
C.P. Robert
  • Function : Author

Abstract

Variance reduction has always been a central issue in Monte Carlo experiments. Population Monte Carlo can be used to this effect, in that a mixture of importance functions, called a D-kernel, can be iteratively optimised to achieve the minimum asymptotic variance for a function of interest among all possible mixtures. The implementation of this iterative scheme is illustrated for the computation of the price of a European option in the Cox-Ingersoll-Ross model.
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Dates and versions

inria-00070316 , version 1 (19-05-2006)

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  • HAL Id : inria-00070316 , version 1

Cite

R. Douc, A. Guillin, Jean-Michel Marin, C.P. Robert. Minimum variance importance sampling via Population Monte Carlo. ESAIM: Probability and Statistics, 2007, 11, pp.427-447. ⟨inria-00070316⟩
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