Beyond Moments: Extending the Maximum Entropy Principle to Feature Distribution Constraints

The maximum entropy principle introduced by Jaynes proposes that a data distribution should maximize the entropy subject to constraints imposed by the available knowledge. Jaynes provided a solution for the case when constraints were imposed on the expected value of a set of scalar functions of the...

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Bibliographic Details
Published inEntropy (Basel, Switzerland) Vol. 20; no. 9; p. 650
Main Author Baggenstoss, Paul M.
Format Journal Article
LanguageEnglish
Published MDPI 30.08.2018
MDPI AG
Subjects
maximum entropy principle
PDF projection
Review
statistical inference
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Summary:The maximum entropy principle introduced by Jaynes proposes that a data distribution should maximize the entropy subject to constraints imposed by the available knowledge. Jaynes provided a solution for the case when constraints were imposed on the expected value of a set of scalar functions of the data. These expected values are typically moments of the distribution. This paper describes how the method of maximum entropy PDF projection can be used to generalize the maximum entropy principle to constraints on the joint distribution of this set of functions.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1099-4300
1099-4300
DOI:10.3390/e20090650