Standard

Conditional Optimization of the Functional Computational Kernel Algorithm for Approximating the Probability Density on the Basis of a Given Sample. / Bulgakova, T. E.; Voytishek, A. V.

In: Computational Mathematics and Mathematical Physics, Vol. 61, No. 9, 3, 09.2021, p. 1401-1415.

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

Harvard

Bulgakova, TE & Voytishek, AV 2021, 'Conditional Optimization of the Functional Computational Kernel Algorithm for Approximating the Probability Density on the Basis of a Given Sample', Computational Mathematics and Mathematical Physics, vol. 61, no. 9, 3, pp. 1401-1415. https://doi.org/10.1134/S0965542521090062

APA

Bulgakova, T. E., & Voytishek, A. V. (2021). Conditional Optimization of the Functional Computational Kernel Algorithm for Approximating the Probability Density on the Basis of a Given Sample. Computational Mathematics and Mathematical Physics, 61(9), 1401-1415. [3]. https://doi.org/10.1134/S0965542521090062

Vancouver

Bulgakova TE, Voytishek AV. Conditional Optimization of the Functional Computational Kernel Algorithm for Approximating the Probability Density on the Basis of a Given Sample. Computational Mathematics and Mathematical Physics. 2021 Sept;61(9):1401-1415. 3. doi: 10.1134/S0965542521090062

Author

Bulgakova, T. E. ; Voytishek, A. V. / Conditional Optimization of the Functional Computational Kernel Algorithm for Approximating the Probability Density on the Basis of a Given Sample. In: Computational Mathematics and Mathematical Physics. 2021 ; Vol. 61, No. 9. pp. 1401-1415.

BibTeX

@article{77ce606b3f11422aa149cfaa752a92dc,
title = "Conditional Optimization of the Functional Computational Kernel Algorithm for Approximating the Probability Density on the Basis of a Given Sample",
abstract = "The problem of obtaining a numerical functional approximation of probability density on the basis of a given or simulated sample values with a prescribed error level at the minimum cost is considered. A computational algorithm for solving this problem that is a functional version of the kernel estimate of the probability density is proposed. This algorithm is similar to the functional computational kernel statistical algorithm for the approximate solution of the Fredholm integral equation of second kind, for which the theory of conditional optimization was earlier built. In this paper, this theory is built for the constructed functional computational kernel algorithm of approximating the probability density.",
keywords = "conditionally optimal parameters, functional computational kernel algorithm, functional computational statistical algorithm, numerical approximation of functions, numerical functional approximation of probability density",
author = "Bulgakova, {T. E.} and Voytishek, {A. V.}",
note = "Publisher Copyright: {\textcopyright} 2021, Pleiades Publishing, Ltd.",
year = "2021",
month = sep,
doi = "10.1134/S0965542521090062",
language = "English",
volume = "61",
pages = "1401--1415",
journal = "Computational Mathematics and Mathematical Physics",
issn = "0965-5425",
publisher = "PLEIADES PUBLISHING INC",
number = "9",

}

RIS

TY - JOUR

T1 - Conditional Optimization of the Functional Computational Kernel Algorithm for Approximating the Probability Density on the Basis of a Given Sample

AU - Bulgakova, T. E.

AU - Voytishek, A. V.

N1 - Publisher Copyright: © 2021, Pleiades Publishing, Ltd.

PY - 2021/9

Y1 - 2021/9

N2 - The problem of obtaining a numerical functional approximation of probability density on the basis of a given or simulated sample values with a prescribed error level at the minimum cost is considered. A computational algorithm for solving this problem that is a functional version of the kernel estimate of the probability density is proposed. This algorithm is similar to the functional computational kernel statistical algorithm for the approximate solution of the Fredholm integral equation of second kind, for which the theory of conditional optimization was earlier built. In this paper, this theory is built for the constructed functional computational kernel algorithm of approximating the probability density.

AB - The problem of obtaining a numerical functional approximation of probability density on the basis of a given or simulated sample values with a prescribed error level at the minimum cost is considered. A computational algorithm for solving this problem that is a functional version of the kernel estimate of the probability density is proposed. This algorithm is similar to the functional computational kernel statistical algorithm for the approximate solution of the Fredholm integral equation of second kind, for which the theory of conditional optimization was earlier built. In this paper, this theory is built for the constructed functional computational kernel algorithm of approximating the probability density.

KW - conditionally optimal parameters

KW - functional computational kernel algorithm

KW - functional computational statistical algorithm

KW - numerical approximation of functions

KW - numerical functional approximation of probability density

UR - http://www.scopus.com/inward/record.url?scp=85117296752&partnerID=8YFLogxK

U2 - 10.1134/S0965542521090062

DO - 10.1134/S0965542521090062

M3 - Article

AN - SCOPUS:85117296752

VL - 61

SP - 1401

EP - 1415

JO - Computational Mathematics and Mathematical Physics

JF - Computational Mathematics and Mathematical Physics

SN - 0965-5425

IS - 9

M1 - 3

ER -

ID: 34569518