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....
| Κύριοι συγγραφείς: | , |
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| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2011.
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| Σειρά: | Intelligent Systems Reference Library,
14 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
