Publikation: Generalized Maximum Entropy Estimation
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2019
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Published
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Journal of Machine Learning Research. Microtome Publishing. 2019, 20, 138. ISSN 1532-4435. eISSN 1533-7928
Zusammenfassung
We 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.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
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Entropy maximization, convex optimization, relative entropy minimization, fast gradient method, approximate dynamic programming
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SUTTER, 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 1533-7928BibTex
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year={2019},
title={Generalized Maximum Entropy Estimation},
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volume={20},
issn={1532-4435},
journal={Journal of Machine Learning Research},
author={Sutter, Tobias and Sutter, David and Esfahani, Peyman Mohajerin and Lygeros, John},
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URL der Originalveröffentl.
Prüfdatum der URL
2021-12-02
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Ja
