Maximum entropy and robust prediction on a simplex

H. Vincent Poor

doi:10.1109/18.761257

Maximum entropy and robust prediction on a simplex

H. Vincent Poor

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

The related problems of (finite-length) robust prediction and maximizing spectral entropy over a simplex of co-variance 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.

Original language	English (US)
Pages (from-to)	1150-1164
Number of pages	15
Journal	IEEE Transactions on Information Theory
Volume	45
Issue number	4
DOIs	https://doi.org/10.1109/18.761257
State	Published - 1999

All Science Journal Classification (ASJC) codes

Information Systems
Computer Science Applications
Library and Information Sciences

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10.1109/18.761257

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title = "Maximum entropy and robust prediction on a simplex",

abstract = "The related problems of (finite-length) robust prediction and maximizing spectral entropy over a simplex of co-variance 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.",

author = "Poor, {H. Vincent}",

note = "Funding Information: Manuscript received September 8, 1996; revised July 15, 1998. This work was supported by the U.K. Science and Engineering Research Council under a Senior Visiting Fellowship at Imperial College, London. The author is with the Electrical Engineering Department, Princeton University, Princeton, NJ 08544 USA (e-mail: poor@princeton.edu). Communicated by A. Hero, Associate Editor for Signal Processing. Publisher Item Identifier S 0018-9448(99)03169-7.",

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PY - 1999

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N2 - The related problems of (finite-length) robust prediction and maximizing spectral entropy over a simplex of co-variance 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.

AB - The related problems of (finite-length) robust prediction and maximizing spectral entropy over a simplex of co-variance 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.

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Maximum entropy and robust prediction on a simplex

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