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https://doi.org/10.1103/physre...
Article . 2020 . Peer-reviewed
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Article . 2019
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When Shannon and Khinchin meet Shore and Johnson: Equivalence of information theory and statistical inference axiomatics

Authors: Petr Jizba; Jan Korbel;

When Shannon and Khinchin meet Shore and Johnson: Equivalence of information theory and statistical inference axiomatics

Abstract

We propose a unified framework for both Shannon-Khinchin and Shore-Johnson axiomatic systems. We do it by rephrasing Shannon-Khinchine axioms in terms of generalized arithmetics of Kolmogorov and Nagumo. We prove that the two axiomatic schemes yield identical classes of entropic functionals -- Uffink class of entropies. This allows to re-establish the entropic parallelism between information theory and statistical inference that has seemed to be "broken" by the use of non-Shannonian entropies.

6 pages, this version was accepted to Phys. Rev. E

Keywords

Statistical Mechanics (cond-mat.stat-mech), Physics - Data Analysis, Statistics and Probability, FOS: Physical sciences, Mathematical Physics (math-ph), Condensed Matter - Statistical Mechanics, Mathematical Physics, Data Analysis, Statistics and Probability (physics.data-an)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Average
Top 10%
Green