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Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 2019, Volume 59, Number 5, Pages 822–828
DOI: https://doi.org/10.1134/S0044466919050119
(Mi zvmmf10894)
 

This article is cited in 3 scientific papers (total in 3 papers)

Improvement of multidimensional randomized Monte Carlo algorithms with “splitting”

G. A. Mikhailovab

a Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630090 Russia
b Novosibirsk State University, Novosibirsk, 630090 Russia
Citations (3)
References:
Abstract: 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 $n$ of baseline trajectories are modeled for each medium realization. The optimal value of $n$ 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.
Key words: probabilistic model, Monte Carlo method, statistical modeling, randomized algorithm, double randomization method, random medium, splitting method, statistical kernel estimate, complexity of functional estimate.
Funding agency Grant number
Russian Foundation for Basic Research 17-01-00823_а
18-01-00356_а
This work was performed within the framework of a state task at the Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Science (project no. 0315-2016-0002) and was supported in part by the Russian Foundation for Basic Research (project nos. 16-01-00530, 17-01-00823, 18-01-00356).
Received: 19.11.2018
Revised: 11.01.2019
Accepted: 11.01.2019
English version:
Computational Mathematics and Mathematical Physics, 2019, Volume 59, Issue 5, Pages 775–781
DOI: https://doi.org/10.1134/S0965542519050117
Bibliographic databases:
Document Type: Article
UDC: 519.676
Language: Russian
Citation: G. A. Mikhailov, “Improvement of multidimensional randomized Monte Carlo algorithms with “splitting””, Zh. Vychisl. Mat. Mat. Fiz., 59:5 (2019), 822–828; Comput. Math. Math. Phys., 59:5 (2019), 775–781
Citation in format AMSBIB
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\pages 775--781
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  • This publication is cited in the following 3 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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    Журнал вычислительной математики и математической физики Computational Mathematics and Mathematical Physics
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    References:15
     
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