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Maximum Entropy Estimation via Gauss-LP Quadratures


M. Thély

Semester Thesis, SS16 (10502)

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 quadrature via an in_nite-dimensional linear program, and utilizes a convex clustering algorithm to compute an approximate solution which requires reduced computational e_ort. It shows to be particularly appealing when looking at problems with unusual domains and in a multi-dimensional setting. We prove the concept by applying our method to an example problem on the unit disk.

Supervisor: Tobias Sutter, Dr. Peyman Mohajerin Esfahani


Type of Publication:

(13)Semester/Bachelor Thesis

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2016:IFA_5471
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