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

Fraunhofer FKIE, Wachtberg 53343, Germany
Entropy 2018, 20(9), 650; https://doi.org/10.3390/e20090650
Received: 27 June 2018 / Revised: 2 August 2018 / Accepted: 20 August 2018 / Published: 30 August 2018
(This article belongs to the Special Issue Entropy: From Physics to Information Sciences and Geometry)
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. View Full-Text
Keywords: maximum entropy principle; PDF projection; statistical inference maximum entropy principle; PDF projection; statistical inference
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MDPI and ACS Style

Baggenstoss, P.M. Beyond Moments: Extending the Maximum Entropy Principle to Feature Distribution Constraints. Entropy 2018, 20, 650. https://doi.org/10.3390/e20090650

AMA Style

Baggenstoss PM. Beyond Moments: Extending the Maximum Entropy Principle to Feature Distribution Constraints. Entropy. 2018; 20(9):650. https://doi.org/10.3390/e20090650

Chicago/Turabian Style

Baggenstoss, Paul M. 2018. "Beyond Moments: Extending the Maximum Entropy Principle to Feature Distribution Constraints" Entropy 20, no. 9: 650. https://doi.org/10.3390/e20090650

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