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Entropy Measures vs. Kolmogorov Complexity
AbstractKolmogorov 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.
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Teixeira, A.; Matos, A.; Souto, A.; Antunes, L. Entropy Measures vs. Kolmogorov Complexity. Entropy 2011, 13, 595-611.View more citation formats
Teixeira A, Matos A, Souto A, Antunes L. Entropy Measures vs. Kolmogorov Complexity. Entropy. 2011; 13(3):595-611.Chicago/Turabian Style
Teixeira, Andreia; Matos, Armando; Souto, André; Antunes, Luís. 2011. "Entropy Measures vs. Kolmogorov Complexity." Entropy 13, no. 3: 595-611.