The minmax information measure

J. N. Kapur; George Baciu; H. K. Kesavan

doi:10.1080/00207729508929020

The minmax information measure

J. N. Kapur, George Baciu, H. K. Kesavan

Research output: Journal article publication › Journal article › Academic research › peer-review

27 Citations (Scopus)

Abstract

The importance of finding minimum entropy probability distributions and the value of minimum entropy for a probabilistic system is discussed. A method to calculate these when there are both moment and inequality constraints on probabilities is given and illustrated with examples. It is shown that: information given by moments or inequalities on probabilities can be measured by the reduction in the uncertainty gap (Smax- Smin); and in certain circumstances the inequalities on probabilities can provide significant information about probabilistic systems.

Original language	English
Pages (from-to)	1-12
Number of pages	12
Journal	International Journal of Systems Science
Volume	26
Issue number	1
DOIs	https://doi.org/10.1080/00207729508929020
Publication status	Published - 1 Jan 1995
Externally published	Yes

ASJC Scopus subject areas

Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications

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10.1080/00207729508929020

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The minmax information measure

Abstract

ASJC Scopus subject areas

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