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

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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.

Translated title of the contributionУсловная оптимизация функционального вычислительного ядерного алгоритма приближения вероятностной плотности по заданной выборке
Original languageEnglish
Article number3
Pages (from-to)1401-1415
Number of pages15
JournalComputational Mathematics and Mathematical Physics
Volume61
Issue number9
DOIs
Publication statusPublished - Sep 2021

Keywords

  • conditionally optimal parameters
  • functional computational kernel algorithm
  • functional computational statistical algorithm
  • numerical approximation of functions
  • numerical functional approximation of probability density

OECD FOS+WOS

  • 1.01 MATHEMATICS
  • 1.03 PHYSICAL SCIENCES AND ASTRONOMY

State classification of scientific and technological information

  • 27.41 Computational mathematics

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