Title :
Maximum entropy and robust prediction on a simplex
Author :
Poor, H. Vincent
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., NJ, USA
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;
Conference_Titel :
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location :
Alexandria, VA
Print_ISBN :
0-7803-2761-6
DOI :
10.1109/WITS.1994.513848
