Generalized Maximum Entropy Estimation

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dc.contributor.authorSutter, Tobias
dc.contributor.authorSutter, David
dc.contributor.authorEsfahani, Peyman Mohajerin
dc.contributor.authorLygeros, John
dc.date.accessioned2021-12-02T11:58:34Z
dc.date.available2021-12-02T11:58:34Z
dc.date.issued2019eng
dc.description.abstractWe consider the problem of estimating a probability distribution that maximizes the entropy while satisfying a finite number of moment constraints, possibly corrupted by noise. Based on duality of convex programming, we present a novel approximation scheme using a smoothed fast gradient method that is equipped with explicit bounds on the approximation error. We further demonstrate how the presented scheme can be used for approximating the chemical master equation through the zero-information moment closure method, and for an approximate dynamic programming approach in the context of constrained Markov decision processes with uncountable state and action spaces.eng
dc.description.versionpublishedeng
dc.identifier.ppn1782618570
dc.identifier.urihttps://kops.uni-konstanz.de/handle/123456789/55731
dc.language.isoengeng
dc.rightsterms-of-use
dc.rights.urihttps://rightsstatements.org/page/InC/1.0/
dc.subjectEntropy maximization, convex optimization, relative entropy minimization, fast gradient method, approximate dynamic programmingeng
dc.subject.ddc004eng
dc.titleGeneralized Maximum Entropy Estimationeng
dc.typeJOURNAL_ARTICLEeng
dspace.entity.typePublication
kops.citation.bibtex
@article{Sutter2019Gener-55731,
  year={2019},
  title={Generalized Maximum Entropy Estimation},
  url={https://jmlr.org/papers/v20/17-486.html},
  volume={20},
  issn={1532-4435},
  journal={Journal of Machine Learning Research},
  author={Sutter, Tobias and Sutter, David and Esfahani, Peyman Mohajerin and Lygeros, John},
  note={Article Number: 138}
}
kops.citation.iso690SUTTER, Tobias, David SUTTER, Peyman Mohajerin ESFAHANI, John LYGEROS, 2019. Generalized Maximum Entropy Estimation. In: Journal of Machine Learning Research. Microtome Publishing. 2019, 20, 138. ISSN 1532-4435. eISSN 1533-7928deu
kops.citation.iso690SUTTER, Tobias, David SUTTER, Peyman Mohajerin ESFAHANI, John LYGEROS, 2019. Generalized Maximum Entropy Estimation. In: Journal of Machine Learning Research. Microtome Publishing. 2019, 20, 138. ISSN 1532-4435. eISSN 1533-7928eng
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kops.sourcefieldJournal of Machine Learning Research. Microtome Publishing. 2019, <b>20</b>, 138. ISSN 1532-4435. eISSN 1533-7928deu
kops.sourcefield.plainJournal of Machine Learning Research. Microtome Publishing. 2019, 20, 138. ISSN 1532-4435. eISSN 1533-7928deu
kops.sourcefield.plainJournal of Machine Learning Research. Microtome Publishing. 2019, 20, 138. ISSN 1532-4435. eISSN 1533-7928eng
kops.urlhttps://jmlr.org/papers/v20/17-486.htmleng
kops.urlDate2021-12-02eng
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source.periodicalTitleJournal of Machine Learning Researcheng
source.publisherMicrotome Publishingeng

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