DocumentCode
2336196
Title
Maximum entropy and robust prediction on a simplex
Author
Poor, H. Vincent
Author_Institution
Dept. of Electr. Eng., Princeton Univ., NJ, USA
fYear
1994
fDate
27-29 Oct 1994
Firstpage
4
Abstract
The related problems of (finite-length) robust prediction and maximizing spectral entropy over a simplex of covariance matrices are considered. General properties of iterative solutions of these problems are developed, and monotone convergence proofs are presented for two algorithms that provide such solutions. The analogous problems for simplexes of spectral densities are also considered
Keywords
convergence of numerical methods; covariance matrices; iterative methods; maximum entropy methods; prediction theory; spectral analysis; algorithms; covariance matrices; finite-length robust prediction; iterative solutions; maximum entropy; monotone convergence proofs; simplex; spectral densities; spectral entropy; Centralized control; Covariance matrix; Data compression; Entropy; Iterative algorithms; Minimax techniques; Predictive models; Robustness; Stochastic processes; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location
Alexandria, VA
Print_ISBN
0-7803-2761-6
Type
conf
DOI
10.1109/WITS.1994.513848
Filename
513848
Link To Document