Maximum Entropy Estimation via Gauss-LP Quadratures <ce:cross-ref refid='fn1'> <ce:sup loc='post'>*</ce:sup> </ce:cross-ref> <ce:footnote id='fn1'> <ce:label>*</ce:label> <ce:note-para id='npara0001' view='all'>Research was supported by the Swiss National Science Foundation under grant ”P2EZP2_165264” and by the European Commission under the project SPEEDD.</ce:note-para> </ce:footnote>

Maxime Thély; Tobias Sutter; Peyman Mohajerin Esfahani; John Lygeros

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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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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.

机译：通过高斯-LP正交最大熵估计 * * 研究得到了瑞士国家自然科学基金会的“ P2EZP2_165264”资助和欧盟委员会下的SPEEDD项目。

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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.

机译：我们为一类参数积分问题提出了一种近似方法，当求解最大熵估计问题的对偶时，这些问题自然就会出现。我们的方法建立在最近通过无穷维线性程序对高斯积分的泛化上，并利用凸聚类算法来计算需要减少计算量的近似解。当研究异常域和多维环境中的问题时，它特别吸引人。作为概念证明，我们将我们的方法应用于单位光盘上的示例问题。

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《IFAC PapersOnLine》 |2017年第1期|共6页
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Maxime Thély; Tobias Sutter; Peyman Mohajerin Esfahani; John Lygeros;
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中图分类自动化技术、计算机技术;
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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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