Adaptive stratified monte carlo algorithm for numerical computation of integrals
For researchers needing accurate numerical integration, this offers an incremental improvement over standard Monte Carlo methods.
The paper proposes an adaptive stratified Monte Carlo algorithm for numerical integration that iteratively splits strata based on variance indicators to reduce variance. Numerical experiments confirm its efficiency.
In this paper, we aim to compute numerical approximation integral 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 big values. The stratification method is based on the optimal allocation strategy in order to decrease the variance from iteration to another. Numerical experiments show and confirm the efficiency of our algorithm.