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Simulation algorithms for stationary sequences with distributions in the form of a mixture of Gaussian distributions. / Akenteva, Marina S.; Kargapolova, Nina A.; Ogorodnikov, Vasily A.

In: Russian Journal of Numerical Analysis and Mathematical Modelling, Vol. 39, No. 3, 06.2024, p. 123-130.

Research output: Contribution to journal › Article › peer-review

Harvard

Akenteva, MS, Kargapolova, NA & Ogorodnikov, VA 2024, 'Simulation algorithms for stationary sequences with distributions in the form of a mixture of Gaussian distributions', Russian Journal of Numerical Analysis and Mathematical Modelling, vol. 39, no. 3, pp. 123-130. https://doi.org/10.1515/rnam-2024-0012

APA

Akenteva, M. S., Kargapolova, N. A., & Ogorodnikov, V. A. (2024). Simulation algorithms for stationary sequences with distributions in the form of a mixture of Gaussian distributions. Russian Journal of Numerical Analysis and Mathematical Modelling, 39(3), 123-130. https://doi.org/10.1515/rnam-2024-0012

Vancouver

Akenteva MS, Kargapolova NA, Ogorodnikov VA. Simulation algorithms for stationary sequences with distributions in the form of a mixture of Gaussian distributions. Russian Journal of Numerical Analysis and Mathematical Modelling. 2024 Jun;39(3):123-130. doi: 10.1515/rnam-2024-0012

Author

Akenteva, Marina S. ; Kargapolova, Nina A. ; Ogorodnikov, Vasily A. / Simulation algorithms for stationary sequences with distributions in the form of a mixture of Gaussian distributions. In: Russian Journal of Numerical Analysis and Mathematical Modelling. 2024 ; Vol. 39, No. 3. pp. 123-130.

BibTeX

@article{c31ec89dc1eb4e19b3fa6bcdaa192908,
title = "Simulation algorithms for stationary sequences with distributions in the form of a mixture of Gaussian distributions",
abstract = "In this paper, we present three algorithms for simulation of intervals of stationary vector and scalar sequences with partial distributions of their subsequences of fixed length in the form of a mixture of Gaussian distributions. The first algorithm is based on superposition of two Gaussian vector processes and the second and third ones use the method of conditional distributions and the method of superpositions to simulate the mixtures and to select realizations for approximate construction of conditional realizations. Some properties of these algorithms are presented.",
keywords = "Stochastic simulation algorithms, conditional distributions, mixture of Gaussian distributions, multidimensional distribution, superposition of random processes",
author = "Akenteva, {Marina S.} and Kargapolova, {Nina A.} and Ogorodnikov, {Vasily A.}",
note = "The research was carried out within the framework of the State Assignment ICM & MG SB RAS FWNM–2022–0002.",
year = "2024",
month = jun,
doi = "10.1515/rnam-2024-0012",
language = "English",
volume = "39",
pages = "123--130",
journal = "Russian Journal of Numerical Analysis and Mathematical Modelling",
issn = "0927-6467",
publisher = "Walter de Gruyter GmbH",
number = "3",

}

RIS

TY - JOUR

T1 - Simulation algorithms for stationary sequences with distributions in the form of a mixture of Gaussian distributions

AU - Akenteva, Marina S.

AU - Kargapolova, Nina A.

AU - Ogorodnikov, Vasily A.

N1 - The research was carried out within the framework of the State Assignment ICM & MG SB RAS FWNM–2022–0002.

PY - 2024/6

Y1 - 2024/6

N2 - In this paper, we present three algorithms for simulation of intervals of stationary vector and scalar sequences with partial distributions of their subsequences of fixed length in the form of a mixture of Gaussian distributions. The first algorithm is based on superposition of two Gaussian vector processes and the second and third ones use the method of conditional distributions and the method of superpositions to simulate the mixtures and to select realizations for approximate construction of conditional realizations. Some properties of these algorithms are presented.

AB - In this paper, we present three algorithms for simulation of intervals of stationary vector and scalar sequences with partial distributions of their subsequences of fixed length in the form of a mixture of Gaussian distributions. The first algorithm is based on superposition of two Gaussian vector processes and the second and third ones use the method of conditional distributions and the method of superpositions to simulate the mixtures and to select realizations for approximate construction of conditional realizations. Some properties of these algorithms are presented.

KW - Stochastic simulation algorithms

KW - conditional distributions

KW - mixture of Gaussian distributions

KW - multidimensional distribution

KW - superposition of random processes

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001237297900005

UR - https://www.mendeley.com/catalogue/4897778b-ec79-3ca8-b69f-575238669df6/

U2 - 10.1515/rnam-2024-0012

DO - 10.1515/rnam-2024-0012

M3 - Article

VL - 39

SP - 123

EP - 130

JO - Russian Journal of Numerical Analysis and Mathematical Modelling

JF - Russian Journal of Numerical Analysis and Mathematical Modelling

SN - 0927-6467

IS - 3

ER -

ID: 61163294