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Minimum variance importance sampling via Population Monte Carlo

R. Douc, A. Guillin, J.-M. Marin, C. P. Robert (2007)

ESAIM: Probability and Statistics

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 optimized 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. A Central Limit theorem as well...

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