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Learning probability distributions from smooth observables and the maximum entropy principle: some remarks
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Learning probability distributions from smooth observables and the maximum entropy principle: some remarks
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Journal Article
Learning probability distributions from smooth observables and the maximum entropy principle: some remarks
2015
Overview
The maximum entropy principle (MEP) is a very useful working hypothesis in a wide variety of inference problems, ranging from biological to engineering tasks. To better understand the reasons of the success of MEP, we propose a statistical-mechanical formulation to treat the space of probability distributions constrained by the measures of (experimental) observables. In this paper we first review the results of a detailed analysis of the simplest case of randomly chosen observables. In addition, we investigate by numerical and analytical means the case of smooth observables, which is of practical relevance. Our preliminary results are presented and discussed with respect to the efficiency of the MEP.
Publisher
IOP Publishing,IOP Science
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