Maximum Entropy Principle in Statistical Inference: Case for Non-Shannonian Entropies

Petr Jizba and Jan Korbel
Phys. Rev. Lett. 122, 120601 – Published 26 March 2019
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Abstract

In this Letter, we show that the Shore-Johnson axioms for the maximum entropy principle in statistical estimation theory account for a considerably wider class of entropic functional than previously thought. Apart from a formal side of the proof where a one-parameter class of admissible entropies is identified, we substantiate our point by analyzing the effect of weak correlations and by discussing two pertinent examples: two-qubit quantum system and transverse-momentum behavior of hadrons in high-energy proton-proton collisions.

  • Received 19 August 2018
  • Revised 12 January 2019

DOI:https://doi.org/10.1103/PhysRevLett.122.120601

© 2019 American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & ThermodynamicsInterdisciplinary PhysicsGeneral PhysicsNuclear Physics

Authors & Affiliations

Petr Jizba1,* and Jan Korbel2,3,1,†

  • 1FNSPE, Czech Technical University in Prague, Břehová 7, 115 19, Prague, Czech Republic
  • 2Section for Science of Complex Systems, CeMSIIS, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria
  • 3Complexity Science Hub Vienna, Josefstädter Strasse 39, 1080 Vienna, Austria

  • *p.jizba@fjfi.cvut.cz
  • jan.korbel@meduniwien.ac.at

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Issue

Vol. 122, Iss. 12 — 29 March 2019

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