Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on Marchuk Scientific Readings 2020, MSR 2020

Год издания: 2021

Идентификатор DOI: 10.1088/1742-6596/1715/1/012061

Аннотация: A new approach to data processing and uncertainty modeling based on the use of computational probabilistic analysis is considered. The basis of computational probabilistic analysis is numerical operations on probability density functions represented by piecewise polynomial functions. The problems of predicting the time series of distributions and estimating the probability densities of solutions of boundary value problems and systems of nonlinear equations with random coefficients are considered. Interval analysis, functional data analysis, symbolic data analysis and Monte Carlo method are currently used to study such data. A comparison of these approaches is given. Numerical examples show the effectiveness of the proposed approaches. © 2021 Institute of Physics Publishing. All rights reserved.

Журнал: Journal of Physics: Conference Series

Выпуск журнала: Vol. 1715, Is. 1

Номера страниц: 12061

ISSN журнала: 17426588

Издатель: IOP Publishing Ltd

• Dobronets B.S. (Institute of Space and Information Technology, Siberian Federal University, Kirenskogo 26, Krasnoyarsk, 660074, Russian Federation)
• Popova O.A. (Institute of Space and Information Technology, Siberian Federal University, Kirenskogo 26, Krasnoyarsk, 660074, Russian Federation)

• Scopus
• Ядро РИНЦ (eLIBRARY.RU)