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On an Objective Basis for the Maximum Entropy Principle

Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
Author to whom correspondence should be addressed.
Entropy 2015, 17(1), 401-406;
Received: 26 September 2014 / Revised: 12 January 2015 / Accepted: 15 January 2015 / Published: 19 January 2015
(This article belongs to the Section Information Theory, Probability and Statistics)
In this letter, we elaborate on some of the issues raised by a recent paper by Neapolitan and Jiang concerning the maximum entropy (ME) principle and alternative principles for estimating probabilities consistent with known, measured constraint information. We argue that the ME solution for the “problematic” example introduced by Neapolitan and Jiang has stronger objective basis, rooted in results from information theory, than their alternative proposed solution. We also raise some technical concerns about the Bayesian analysis in their work, which was used to independently support their alternative to the ME solution. The letter concludes by noting some open problems involving maximum entropy statistical inference. View Full-Text
Keywords: maximum entropy; asymptotic equipartition principle maximum entropy; asymptotic equipartition principle
MDPI and ACS Style

Miller, D.J.; Soleimani, H. On an Objective Basis for the Maximum Entropy Principle. Entropy 2015, 17, 401-406.

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