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Comment on "R\'enyi entropy yields artifficial biases not in the data and incorrect updating due to the infinite-size data"

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arxiv 1905.00729 v1 pith:UEH5HVKU submitted 2019-05-01 physics.data-an

classification physics.data-an
keywords axiomsbagcicommentdataentropyenyiissuesjohnson
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In their recent paper [Phys. Rev. E 99 (2019) 032134], T. Oikinomou and B. Bagci have argued that R\'enyi entropy is ill-suited for inference purposes because it is not consistent with the Shore{ Johnson axioms of statistical estimation theory. In this Comment we seek to clarify the latter statement by showing that there are several issues in Oikinomou{Bagci reasonings which lead to erroneous conclusions. When all these issues are properly accounted for, no violation of Shore- Johnson axioms is found.

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  1. Reply to "Comment on `R\'enyi entropy yields artificial biases not in the data and incorrect updating due to the finite-size data' "

    cond-mat.stat-mech 2019-08 conditional novelty 4.0 of 10

    Renyi entropy violates the Shore-Johnson subset-independence axiom, so using it for maximum-entropy inference with linear constraints introduces biases not present in the data.

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