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

 
: Baggenstoss, P.M.

:
Volltext ()

Entropy. Online journal 20 (2018), Nr.9, Art.650, 13 S.
http://www.mdpi.com/journal/entropy
ISSN: 1099-4300
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer FKIE ()

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-524406.html