Improvement of Multidimensional Randomized Monte Carlo Algorithms with “Splitting”

Randomized Monte Carlo algorithms are constructed by jointly realizing a baseline probabilistic model of the problem and its random parameters (random medium) in order to study a parametric distribution of linear functionals. This work relies on statistical kernel estimation of the multidimensional...

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Published in:Computational mathematics and mathematical physics 2019-05, Vol.59 (5), p.775-781
Main Author: Mikhailov, G. A.
Format: Article
Language:English
Subjects:
Algorithms
Computational Mathematics and Numerical Analysis
Computer simulation
Kernels
Mathematics
Mathematics and Statistics
Order parameters
Probabilistic models
Randomization
Splitting
Statistical analysis
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description Randomized Monte Carlo algorithms are constructed by jointly realizing a baseline probabilistic model of the problem and its random parameters (random medium) in order to study a parametric distribution of linear functionals. This work relies on statistical kernel estimation of the multidimensional distribution density with a “homogeneous” kernel and on a splitting method, according to which a certain number of baseline trajectories are modeled for each medium realization. The optimal value of is estimated using a criterion for computational complexity formulated in this work. Analytical estimates of the corresponding computational efficiency are obtained with the help of rather complicated calculations.
doi_str_mv 10.1134/S0965542519050117
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subjects Algorithms
Computational Mathematics and Numerical Analysis
Computer simulation
Kernels
Mathematics
Mathematics and Statistics
Order parameters
Probabilistic models
Randomization
Splitting
Statistical analysis
title Improvement of Multidimensional Randomized Monte Carlo Algorithms with “Splitting”
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