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Conditional Optimization of Algorithms for Estimating Distributions of Solutions to Stochastic Differential Equations. / Averina, Tatyana.
в: Mathematics, Том 12, № 4, 586, 02.2024.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Conditional Optimization of Algorithms for Estimating Distributions of Solutions to Stochastic Differential Equations
AU - Averina, Tatyana
PY - 2024/2
Y1 - 2024/2
N2 - This article discusses an alternative method for estimating marginal probability densities of the solution to stochastic differential equations (SDEs). Two algorithms for calculating the numerical–statistical projection estimate for distributions of solutions to SDEs using Legendre polynomials are proposed. The root-mean-square error of this estimate is studied as a function of the projection expansion length, while the step of a numerical method for solving SDE and the sample size for expansion coefficients are fixed. The proposed technique is successfully verified on three one-dimensional SDEs that have stationary solutions with given one-dimensional distributions and exponential correlation functions. A comparative analysis of the proposed method for calculating the numerical–statistical projection estimate and the method for constructing the histogram is carried out.
AB - This article discusses an alternative method for estimating marginal probability densities of the solution to stochastic differential equations (SDEs). Two algorithms for calculating the numerical–statistical projection estimate for distributions of solutions to SDEs using Legendre polynomials are proposed. The root-mean-square error of this estimate is studied as a function of the projection expansion length, while the step of a numerical method for solving SDE and the sample size for expansion coefficients are fixed. The proposed technique is successfully verified on three one-dimensional SDEs that have stationary solutions with given one-dimensional distributions and exponential correlation functions. A comparative analysis of the proposed method for calculating the numerical–statistical projection estimate and the method for constructing the histogram is carried out.
KW - Legendre polynomials
KW - histogram
KW - marginal probability density
KW - numerical–projection estimate
KW - stochastic differential equations
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85187267712&origin=inward&txGid=42a7af55e7d25a9b404a655621e48a8f
UR - https://www.mendeley.com/catalogue/70eef43f-7c2a-3899-92ef-ea5453c096ee/
U2 - 10.3390/math12040586
DO - 10.3390/math12040586
M3 - Article
VL - 12
JO - Mathematics
JF - Mathematics
SN - 2227-7390
IS - 4
M1 - 586
ER -
ID: 60775412