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Adaptive stratified Monte Carlo algorithm for numerical computation of integrals

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  • Sayah, Toni

Abstract

In this paper, we aim to compute numerical approximations of the integral of a function by using an adaptive Monte Carlo algorithm. We propose a stratified sampling algorithm based on an iterative method which splits the strata following some quantities called indicators which indicate where the variance takes relative large values. The stratification method is based on the optimal allocation strategy in order to decrease the variance from one iteration to another. Numerical experiments show and confirm the efficiency of our algorithm.

Suggested Citation

  • Sayah, Toni, 2019. "Adaptive stratified Monte Carlo algorithm for numerical computation of integrals," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 157(C), pages 143-158.
    • Handle: RePEc:eee:matcom:v:157:y:2019:i:c:p:143-158
    DOI: 10.1016/j.matcom.2018.10.004
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    References listed on IDEAS

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    1. Pierre Étoré & Benjamin Jourdain, 2010. "Adaptive Optimal Allocation in Stratified Sampling Methods," Methodology and Computing in Applied Probability, Springer, vol. 12(3), pages 335-360, September.
    2. Pierre Etore & Gersende Fort & Benjamin Jourdain & Eric Moulines, 2011. "On adaptive stratification," Annals of Operations Research, Springer, vol. 189(1), pages 127-154, September.
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