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Entropy inequalities for classes of probability distributions I. The univariate case

Published online by Cambridge University Press:  01 July 2016

Samuel Karlin  and
Yosef Rinott
Samuel Karlin*
Affiliation:
Stanford University
Yosef Rinott*
Affiliation:
Stanford University
*
∗ Postal address: Department of Mathematics, Stanford University, Stanford CA 94305, U.S.A.
∗ Postal address: Department of Mathematics, Stanford University, Stanford CA 94305, U.S.A.
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Abstract

Entropy functionals of probability densities feature importantly in classifying certain finite-state stationary stochastic processes, in discriminating among competing hypotheses, in characterizing Gaussian, Poisson, and other densities, in describing information processes, and in other contexts. Two general types of problems are considered. For a given parametric family of densities the member of maximal (or sometimes minimal) entropy is ascertained. Secondly, we determine a natural (partial) ordering over for which the entropy functional is monotone. The examples include the multiparameter binomial, multiparameter negative binomial, some classes of log concave densities, and others.

Keywords

ENTROPY FUNCTIONALMAJORIZATION ORDERINGTOTAL POSITIVITYLOG CONCAVITY

Information

Type
Research Article
Information
Advances in Applied Probability , Volume 13 , Issue 1 , March 1981 , pp. 93 - 112
Copyright
Copyright © Applied Probability Trust 1981 

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Footnotes

Yosef Rinott is also a member of the Department of Statistics, The Hebrew University, Jerusalem, Israel.

Supported in part by NIH Grant 2R01 GM10452-16 and NSF Grant MCS 76-80624-A02.

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