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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Detalles Bibliográficos
Autor principal: Baggenstoss, Paul M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513173/
https://www.ncbi.nlm.nih.gov/pubmed/33265739
http://dx.doi.org/10.3390/e20090650

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513173/
https://www.ncbi.nlm.nih.gov/pubmed/33265739
http://dx.doi.org/10.3390/e20090650

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