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Entropy Measures vs. Kolmogorov Complexity

Computer Science Department, Faculty of Sciences, University of Porto, Rua Campo Alegre 1021/1055, 4169-007 Porto, Portugal
Instituto de Telecomunicações, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
Laboratório de Inteligência Artificial e Ciência de Computadores, Rua Campo Alegre 1021/1055, 4169-007 Porto, Portugal
Author to whom correspondence should be addressed.
Entropy 2011, 13(3), 595-611;
Received: 14 January 2011 / Revised: 25 February 2011 / Accepted: 26 February 2011 / Published: 3 March 2011
(This article belongs to the Special Issue Kolmogorov Complexity)
Kolmogorov complexity and Shannon entropy are conceptually different measures. However, for any recursive probability distribution, the expected value of Kolmogorov complexity equals its Shannon entropy, up to a constant. We study if a similar relationship holds for R´enyi and Tsallis entropies of order α, showing that it only holds for α = 1. Regarding a time-bounded analogue relationship, we show that, for some distributions we have a similar result. We prove that, for universal time-bounded distribution mt(x), Tsallis and Rényi entropies converge if and only if α is greater than 1. We also establish the uniform continuity of these entropies. View Full-Text
Keywords: Kolmogorov complexity; Shannon entropy; Rényi entropy; Tsallis entropy Kolmogorov complexity; Shannon entropy; Rényi entropy; Tsallis entropy
MDPI and ACS Style

Teixeira, A.; Matos, A.; Souto, A.; Antunes, L. Entropy Measures vs. Kolmogorov Complexity. Entropy 2011, 13, 595-611.

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