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...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0965-5425
1555-6662
DOI:10.1134/S0965542519050117