Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.

  

Maximum Entropy Estimation via Gauss-LP Quadratures

Author(s):

M. Thély
Conference/Journal:

Semester Thesis, SS16 (10502)
Abstract:

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

Year:

2016
Type of Publication:

(13)Semester/Bachelor Thesis
Supervisor:



File Download:

Request a copy of this publication.
(Uses JavaScript)
% Autogenerated BibTeX entry
@PhdThesis { Xxx:2016:IFA_5471
}
Permanent link