The Numerical Probabilistic Approach to the Processing and Presentation of Remote Monitoring Data

Dobronets, Boris S.; Popova, Olga A.

doi:10.17516/1999-494x-2016-9-7-960-971

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…This work continues the research begun in [1]. As a new mathematical branch, the linear regression for interval-valued and histogram-valued variables presents a new study for uncertainty data processing [2][3][4][5].…”

Section: Introductionmentioning

confidence: 88%

“…Next, we calculate the derivative with respect to a for probability density function ax y f + of summa ax y + . Consequently ( ) p x y , is a polynomial (1) (2) 1 2…”

Section: Numerical Arithmetic For Probability Density Functionmentioning

confidence: 99%

“…The integral (1) will be calculated numerically. Then we define the are the coordinates of the projection on the axis x 1 of the segment(1)…”

mentioning

confidence: 99%

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Piecewise Polynomial Models for Aggregation and Regression Analysis in Remote Sensing of the Earth Problems

Popova¹

2018

J. Sib. Fed. Univ. Eng. technol.

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We discuss the procedures of data aggregation as a preprocessing stage for subsequent to regression modeling. An important feature of study is demonstration of the way how represent the aggregated data. It is proposed to use piecewise polynomial models, including spline aggregate functions. We show that the proposed approach to data aggregation can be interpreted as the frequency distribution. To study its properties density function concept is used. Applying data aggregation models as input and output variables we propose a new probability density function value linear regression model (Distributions Regression). To calculate the data aggregation and regression model we employ numerical probabilistic analysis (NPA). To demonstrate the degree of the correspondence of the proposed methods to reality, we developed a theoretical framework and considered numerical examples.

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Section: Introductionmentioning

confidence: 88%

“…Next, we calculate the derivative with respect to a for probability density function ax y f + of summa ax y + . Consequently ( ) p x y , is a polynomial (1) (2) 1 2…”

Section: Numerical Arithmetic For Probability Density Functionmentioning

confidence: 99%

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Piecewise Polynomial Models for Aggregation and Regression Analysis in Remote Sensing of the Earth Problems

Popova¹

2018

J. Sib. Fed. Univ. Eng. technol.

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“…Let A = (a i j ) be random matrix n = 2. The elements of the matrix A are independent and distributed on the triangular law, a 11 , a 22 distributed on the interval [2,4], a 21 , a 12 distributed on the interval [−1, 1]. The vector b consists of independent components b 1 , b 2 , they are distributed according to a triangular law on the interval [0, 2].…”

Section: Systems Of Linear Algebraic Equationsmentioning

confidence: 99%

Numerical Analysis for Problems of Remote Sensing with Random Input Data

Dobronets

Popova

2019

E3S Web Conf.

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The study is devoted to the remote sensing data processing using the models with random input data. In this article we propose a new approach to calculation of functions with random arguments, which is a technique of fast computations, based on the idea of parallel computations and the application of numerical probability analysis. To calculate a function with random arguments we apply one of the basic concepts of numerical probabilistic analysis as the probabilistic extension. To implement the technique of fast computations, a new method based on parallel recursive calculations is proposed.

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Computational Probabilistic Analysis and Transformation Method in the Earth Remote Research

2020

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The article deals with the issues of numerical modeling of problems with random input data. Finding the joint probability density function of the vector of output parameters is considered. It is proposed to use computational probabilistic analysis and the transformation method. A numerical example of the joint probability density function of the vector of a solution of a system of nonlinear equations with random input data is given.

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The Numerical Probabilistic Approach to the Processing and Presentation of Remote Monitoring Data

Abstract: The paper deals with a numerical probabilistic analysis as a method of processing and presentation

Cited by 4 publications

References 3 publications

Piecewise Polynomial Models for Aggregation and Regression Analysis in Remote Sensing of the Earth Problems

Piecewise Polynomial Models for Aggregation and Regression Analysis in Remote Sensing of the Earth Problems

Numerical Analysis for Problems of Remote Sensing with Random Input Data

Computational Probabilistic Analysis and Transformation Method in the Earth Remote Research

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