Тип публикации: статья из журнала

Год издания: 2024

Идентификатор DOI: 10.1515/mcma-2024-2006

Аннотация: <jats:title>Abstract</jats:title> <jats:p>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 Показать полностьюapproach 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.</jats:p>

Журнал: Monte Carlo Methods and Applications

Выпуск журнала: Т. 0

ISSN журнала: 09299629

Издатель: Brill Academic Publishers

• 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)

• Ядро РИНЦ (eLIBRARY.RU)