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A Characterization of Entropy in Terms of Information Loss
Entropy, 2011 There are numerous characterizations of Shannon entropy and Tsallis entropy as measures of information obeying certain properties. Using work by Faddeev and Furuichi, we derive a very simple characterization.
Tom Leinster, John C. Baez, Tobias Fritz
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Generalizing Information to the Evolution of Rational Belief
Entropy, 2020 Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcome’s plausibility.
Jed A. Duersch, Thomas A. Catanach
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Shannon entropy and complexity measures for Bohr Hamiltonian with triaxial nuclei
Results in Physics, 2022 The Shannon entropy, disequilibrium, and complexity measures for n=0,1 in position and momentum space are investigated within the framework of Bohr Hamiltonian for triaxial nuclei with Davidson potential. The effect of the angular momentum quantum number
P.O. Amadi +8 more
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, 2017 We introduce notions of information/entropy and information loss associated to exponentiable motivic measures. We show that they satisfy appropriate analogs to the Khinchin-type properties that characterize information loss in the context of measures on ...
Marcolli, Matilde
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Relations Between Conditional Shannon Entropy and Expectation of $\ell_{\alpha}$-Norm
, 2016 The paper examines relationships between the conditional Shannon entropy and the expectation of $\ell_{\alpha}$-norm for joint probability distributions.
Iwata, Ken-ichi, Sakai, Yuta
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JIDT: An information-theoretic toolkit for studying the dynamics of complex systems [PDF]
, 2014 Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon)
Lizier, Joseph T.
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A modified belief entropy in Dempster-Shafer framework. [PDF]
PLoS ONE, 2017 How to quantify the uncertain information in the framework of Dempster-Shafer evidence theory is still an open issue. Quite a few uncertainty measures have been proposed in Dempster-Shafer framework, however, the existing studies mainly focus on the mass
Deyun Zhou, Yongchuan Tang, Wen Jiang
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Quantifying Information Leakage in Finite Order Deterministic Programs [PDF]
, 2010 Information flow analysis is a powerful technique for reasoning about the sensitive information exposed by a program during its execution. While past work has proposed information theoretic metrics (e.g., Shannon entropy, min-entropy, guessing entropy ...
Srivatsa, Mudhakar, Zhu, Ji
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On Convergence Properties of Shannon Entropy
, 2007 Convergence properties of Shannon Entropy are studied. In the differential setting, it is shown that weak convergence of probability measures, or convergence in distribution, is not enough for convergence of the associated differential entropies.
A. Antos +16 more
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Does Amount of Information Support Aesthetic Values?
Frontiers in Neuroscience, 2022 Obtaining information from the world is important for survival. The brain, therefore, has special mechanisms to extract as much information as possible from sensory stimuli.
Norberto M. Grzywacz +3 more
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