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An Approximate Algorithm for Simulating Stationary Discrete Random Processes with Bivariate Distributions of Their Consecutive Components in the Form of Mixtures of Gaussian Distributions. / Ogorodnikov, V. A.; Akenteva, M. S.; Kargapolova, N. A.

в: Numerical Analysis and Applications, Том 17, № 2, 06.2024, стр. 169-173.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Ogorodnikov, VA, Akenteva, MS & Kargapolova, NA 2024, 'An Approximate Algorithm for Simulating Stationary Discrete Random Processes with Bivariate Distributions of Their Consecutive Components in the Form of Mixtures of Gaussian Distributions', Numerical Analysis and Applications, Том. 17, № 2, стр. 169-173. https://doi.org/10.1134/S199542392402006X

APA

Ogorodnikov, V. A., Akenteva, M. S., & Kargapolova, N. A. (2024). An Approximate Algorithm for Simulating Stationary Discrete Random Processes with Bivariate Distributions of Their Consecutive Components in the Form of Mixtures of Gaussian Distributions. Numerical Analysis and Applications, 17(2), 169-173. https://doi.org/10.1134/S199542392402006X

Vancouver

Ogorodnikov VA, Akenteva MS, Kargapolova NA. An Approximate Algorithm for Simulating Stationary Discrete Random Processes with Bivariate Distributions of Their Consecutive Components in the Form of Mixtures of Gaussian Distributions. Numerical Analysis and Applications. 2024 июнь;17(2):169-173. doi: 10.1134/S199542392402006X

Author

Ogorodnikov, V. A. ; Akenteva, M. S. ; Kargapolova, N. A. / An Approximate Algorithm for Simulating Stationary Discrete Random Processes with Bivariate Distributions of Their Consecutive Components in the Form of Mixtures of Gaussian Distributions. в: Numerical Analysis and Applications. 2024 ; Том 17, № 2. стр. 169-173.

BibTeX

@article{6a4faeeae2574e30a55195f3d690b1ba,
title = "An Approximate Algorithm for Simulating Stationary Discrete Random Processes with Bivariate Distributions of Their Consecutive Components in the Form of Mixtures of Gaussian Distributions",
abstract = "Abstract: The paper presents an approximate algorithm for modeling a stationary discrete random process with marginal and bivariate distributions of its consecutive components in the form of a mixture of two Gaussian distributions. The algorithm is based on a combination of the conditional distribution method and the rejection method. An example of application of the proposed algorithm for simulating time series of daily maximum air temperatures is given.",
keywords = "bivariate distribution, maximum daily temperature, mixture of Gaussian distributions, stochastic simulation",
author = "Ogorodnikov, {V. A.} and Akenteva, {M. S.} and Kargapolova, {N. A.}",
year = "2024",
month = jun,
doi = "10.1134/S199542392402006X",
language = "English",
volume = "17",
pages = "169--173",
journal = "Numerical Analysis and Applications",
issn = "1995-4239",
publisher = "Maik Nauka-Interperiodica Publishing",
number = "2",

}

RIS

TY - JOUR

T1 - An Approximate Algorithm for Simulating Stationary Discrete Random Processes with Bivariate Distributions of Their Consecutive Components in the Form of Mixtures of Gaussian Distributions

AU - Ogorodnikov, V. A.

AU - Akenteva, M. S.

AU - Kargapolova, N. A.

PY - 2024/6

Y1 - 2024/6

N2 - Abstract: The paper presents an approximate algorithm for modeling a stationary discrete random process with marginal and bivariate distributions of its consecutive components in the form of a mixture of two Gaussian distributions. The algorithm is based on a combination of the conditional distribution method and the rejection method. An example of application of the proposed algorithm for simulating time series of daily maximum air temperatures is given.

AB - Abstract: The paper presents an approximate algorithm for modeling a stationary discrete random process with marginal and bivariate distributions of its consecutive components in the form of a mixture of two Gaussian distributions. The algorithm is based on a combination of the conditional distribution method and the rejection method. An example of application of the proposed algorithm for simulating time series of daily maximum air temperatures is given.

KW - bivariate distribution

KW - maximum daily temperature

KW - mixture of Gaussian distributions

KW - stochastic simulation

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85195129052&origin=inward&txGid=05ff90031b2dd79a1e0456bf9b3bc1e5

UR - https://www.mendeley.com/catalogue/5b18fcec-cf74-326a-a209-6d8395ec7425/

U2 - 10.1134/S199542392402006X

DO - 10.1134/S199542392402006X

M3 - Article

VL - 17

SP - 169

EP - 173

JO - Numerical Analysis and Applications

JF - Numerical Analysis and Applications

SN - 1995-4239

IS - 2

ER -

ID: 61117637