Joint application of the Monte Carlo method and computational probabilistic analysis in problems of numerical modeling with data uncertainties

Dobronets Boris () and Popova Olga ()
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Dobronets Boris: Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, Russia
Popova Olga: Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, Russia

Monte Carlo Methods and Applications, 2024, vol. 30, issue 3, 217-223

Abstract: In this paper, we suggest joint application of computational probabilistic analysis and the Monte Carlo method for numerical stochastic modeling problems. We use all the capabilities of computational probabilistic analysis while maintaining all the advantages of the Monte Carlo method. Our approach allows us to efficiently implement a computational hybrid scheme. In this way, we reduce the computation time and present the results in the form of distributions. The crucial new points of our method are arithmetic operations on probability density functions and procedures for constructing on the probabilistic extensions. Relying on specific numerical examples of solving systems of linear algebraic equations with random coefficients, we present the advantages of our approach.

Keywords: Monte Carlo method; computational probabilistic analysis; probabilistic extensions; numerical hybrid scheme; linear algebraic equations with random coefficients (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/mcma-2024-2006

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