Results 251 to 260 of about 176,073,457 (290)

Entropy Measures and Views of Information

2016
Among the countless papers written by Ronald R. Yager, those on Entropies and measures of information are considered, keeping in mind the notion of view of a set, in order to point out a similarity between the quantities introduced in various frameworks to evaluate a kind of entropy.
Bouchon-Meunier, Bernadette, Marsala, Christophe   +1 more
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Information structures and entropy measurement for a fuzzy probabilistic information system

Journal of Intelligent & Fuzzy Systems, 2021
An information system (IS), an important model in the field of artificial intelligence, takes information structure as the basic structure. A fuzzy probabilistic information system (FPIS) is the combination of some fuzzy relations in the same universe that satisfy probability distribution.
Yanling He, Chunji Yao
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Entropies as measures of software information

Proceedings IEEE International Conference on Software Maintenance. ICSM 2001, 2002
This paper investigates the use of entropies as measures of software information content. Several entropies, including the well-known Shannon entropy, are characterized by their mathematical properties. Based on these characterizations, the entropies, which are suitable for measuring software systems, are rigorously chosen.
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Information and Entropy in Quantum Measurement Processes

International Journal of Theoretical Physics, 1998
Entropy of a physical system is one of the most important quantities in thermodynamics and statistical mechanics. Moreover, there is a relation between entropy and information shown by Szilard in 1929, and by Shannon in 1948. Quantum mechanical entropy was introduced by von Neumann in 1955, as \(S(\widehat \rho) =-Tr(\widehat \rho \ln \widehat \rho)\),
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Information Meaning of Entropy of Nonergodic Measures

Differential Equations, 2019
The main aim of this paper is to study the limit frequency properties of trajectories of the simplest dynamical system generated by the left shift on the space of sequences of letters from a finite alphabet. More precisely, a modification of the Shannon-McMillan-Breiman theorem is proved: for any invariant (not necessarily ergodic) probability measure \
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Measuring information transfer by dispersion transfer entropy

Communications in Nonlinear Science and Numerical Simulation, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Boyi Zhang, Pengjian Shang
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Information entropy as a measure of DEM quality

Computers & Geosciences, 2012
The Shannon-Weaver Information statistic has been proposed as a useful measure of the quality of a Digital Elevation Model. However the statistic, usually referred to as entropy, is based purely on the range of values in a dataset and their relative proportion and is not directly related to the accuracy of those values or their spatial arrangement ...
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Information‐theoretical entropy as a measure of sequence variability

Proteins: Structure, Function, and Bioinformatics, 1991
AbstractWe propose the use of the information‐theoretical entropy, S = −Σpi log2 Pi, as a measure of variability at a given position in a set of aligned sequences. pi stands for the fraction of times the i‐th type appears at a position. For protein sequences, the sum has up to 20 terms, for nucleotide sequences, up to 4 terms, and for codon sequences ...
P S, Shenkin, B, Erman, L D, Mastrandrea
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Relative Entropy as a Measure of Diagnostic Information

Medical Decision Making, 1999
Relative entropy is a concept within information theory that provides a measure of the distance between two probability distributions. The author proposes that the amount of information gained by performing a diagnostic test can be quantified by calculating the relative entropy between the posttest and pretest probability distributions. This statistic,
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Generalizations of Entropy and Information Measures

2015
This paper presents and discusses two generalized forms of the Shannon entropy, as well as a generalized information measure. These measures are applied on a exponential-power generalization of the usual Normal distribution, emerged from a generalized form of the Fisher’s entropy type information measure, essential to Cryptology.
Thomas L. Toulias, Christos P. Kitsos
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