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Maximum Entropy Estimation via Gauss-LP Quadratures

Author(s):

M. Thély, T. Sutter, P. Mohajerin Esfahani, J. Lygeros
Conference/Journal:

IFAC World Congress
Abstract:

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.

Year:

2017
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { Th_Etal:2017:IFA_5618,
    author={M. Th{\'e}ly and T. Sutter and P. Mohajerin Esfahani and J. Lygeros},
    title={{Maximum Entropy Estimation via Gauss-LP Quadratures}},
    booktitle={IFAC World Congress},
    pages={},
    year={2017},
    address={},
    month=jul,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=5618}
}
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