Publikation: Maximum Entropy Estimation via Gauss-LP Quadratures
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2017
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IFAC-PapersOnLine. Elsevier. 2017, 50(1), S. 10470-10475. eISSN 1474-6670. Verfügbar unter: doi: 10.1016/j.ifacol.2017.08.1977
Zusammenfassung
We present an approximation method to a class of parametric integration problems that naturally appear when solving the dual of the maximum entropy estimation problem. Our method builds up on a recent generalization of Gauss quadratures via an infinite-dimensional linear program, and utilizes a convex clustering algorithm to compute an approximate solution which requires reduced computational effort. It shows to be particularly appealing when looking at problems with unusual domains and in a multi-dimensional setting. As a proof of concept we apply our method to an example problem on the unit disc.
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004 Informatik
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Entropy maximization, convex clustering, linear programming, importance sampling
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THÉLY, Maxime, Tobias SUTTER, Peyman Mohajerin ESFAHANI, John LYGEROS, 2017. Maximum Entropy Estimation via Gauss-LP Quadratures. In: IFAC-PapersOnLine. Elsevier. 2017, 50(1), S. 10470-10475. eISSN 1474-6670. Verfügbar unter: doi: 10.1016/j.ifacol.2017.08.1977BibTex
@article{Thely2017Maxim-55739,
year={2017},
doi={10.1016/j.ifacol.2017.08.1977},
title={Maximum Entropy Estimation via Gauss-LP Quadratures},
number={1},
volume={50},
journal={IFAC-PapersOnLine},
pages={10470--10475},
author={Thély, Maxime and Sutter, Tobias and Esfahani, Peyman Mohajerin and Lygeros, John}
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