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Statistical Manifold and Entropy-Based Interence

2018-04-18 09:03    

报告人:Zhang Jun 【】

时   间:2018-04-12 16:00-17:00

地   点:南开大学省身楼216教室


报告内容介绍

    Information Geometry is the differential geometric study of the manifold of probability models, and promises to be a unifying geometric framework for investigating statistical inference, information theory, machine learning, etc. Instead of using metric for measuring distances on such manifolds, these applications often use "divergence functions" for measuring proximity of two points (that do not impose symmetry and triangular inequality). Divergence functions are tied to generalized entropy and cross-entropy functions widely used in machine learning and information sciences.

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