文章基本信息

标题：Maximum Entropy Estimation via Gauss-LP Quadratures * * Research was supported by the Swiss National Science Foundation under grant ”P2EZP2_165264” and by the European Commission under the project SPEEDD.
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作者：Maxime Thély ; Tobias Sutter ; Peyman Mohajerin Esfahani 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：10470-10475
DOI：10.1016/j.ifacol.2017.08.1977
语种：English
出版社：Elsevier
摘要：AbstractWe 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.
关键词：KeywordsEntropy maximizationconvex clusteringlinear programmingimportance sampling