Towards Intelligent Modeling: Statistical Approximation Theory
The main idea of statistical convergence is to demand convergence only for a majority of elements of a sequence. This method of convergence has been investigated in many fundamental areas of mathematics such as: measure theory, approximation theory, fuzzy logic theory, summability theory, and so on....
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2011, 2011
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| Edition: | 1st ed. 2011 |
| Series: | Intelligent Systems Reference Library
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Introduction
- Statistical Approximation by Bivariate Picard Singular Integral Operators
- Uniform Approximation in Statistical Sense by Bivariate Gauss-Weierstrass Singular Integral Operators
- Statistical Lp-Convergence of Bivariate Smooth Picard Singular Integral Operators
- Statistical Lp-Approximation by Bivariate Gauss-Weierstrass Singular Integral Operators
- A Baskakov-Type Generalization of Statistical Approximation Theory
- Weighted Approximation in Statistical Sense to Derivatives of Functions
- Statistical Approximation to Periodic Functions by a General Family of Linear Operators
- Relaxing the Positivity Condition of Linear Operators in Statistical Korovkin Theory
- Statistical Approximation Theory for Stochastic Processes
- Statistical Approximation Theory for Multivariate Stochas tic Processes