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Entropy estimation using the principle of maximum entropy
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011In this paper, we present a novel entropy estimator for a given set of samples drawn from an unknown probability density function (PDF). Counter to other entropy estimators, the estimator presented here is parametric. The proposed estimator uses the maximum entropy principle to offer anm-term approximation to the underlying distribution and does not ...
Behrouz Behmardi +2 more
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Axiomatic Derivation of the Principle of Maximum Entropy and the Principle of Minimum Cross-Entropy [PDF]
IEEE Transactions on Information Theory, 1980 Jaynes's principle of maximum entropy and Kullbacks principle of minimum cross-entropy (minimum directed divergence) are shown to be uniquely correct methods for inductive inference when new information is given in the form of expected values. Previous justifications use intuitive arguments and rely on the properties of entropy and cross-entropy as ...
J Shore
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Probability distributions and the maximum entropy principle
Applied Mathematics and Computation, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
José Villa-Morales, Luis Rincón
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The principle of maximum entropy
The Mathematical Intelligencer, 1985The authors point out that the ''principle of maximum entropy'' can be considered as a variational principle which has applications in statistical mechanics, in decision theory, in pattern-recognition and in time-series analysis. They explain this principle as follows: From the set of all probability distributions (for instance, the possible ...
Guiasu, Silviu, Shenitzer, Abe
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1997
The concept of information can be successfully utilized for the adaptation of a probability distribution to empirical data. In order to proceed to the formulation of the corresponding principle, let us first recall the expression for the empirical probability density for the case when all the samples are distinct $$fe\left( x \right) = \frac{1}{N ...
Igor Grabec, Wolfgang Sachse
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The concept of information can be successfully utilized for the adaptation of a probability distribution to empirical data. In order to proceed to the formulation of the corresponding principle, let us first recall the expression for the empirical probability density for the case when all the samples are distinct $$fe\left( x \right) = \frac{1}{N ...
Igor Grabec, Wolfgang Sachse
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2020
The description of a physical system requires the knowledge of some information, for example the prediction of the motion of a point particle in classical mechanics requires the knowledge of its initial position and momentum besides, of course, the system of forces acting upon it. In the case of a great quantity of particles, i.e.
Vito Dario Camiola +2 more
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The description of a physical system requires the knowledge of some information, for example the prediction of the motion of a point particle in classical mechanics requires the knowledge of its initial position and momentum besides, of course, the system of forces acting upon it. In the case of a great quantity of particles, i.e.
Vito Dario Camiola +2 more
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Adaptive Applications of Maximum Entropy Principle
2020The probability distribution of a system can be adaptively derived using the maximum entropy principle subject to its information set in terms of probabilistic moments. The obtained probability distribution characterizes the wide range of exponential family of distributions when one maximizes Shannon entropy.
Amit Kumar Singh +3 more
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Entropy and Principle of Maximum Entropy
1998Clausius coined the term ‘entropy’ from the Greek meaning transformation. Thus, entropy originated in physics and occupies an exceptional position among physical quantities. It does not appear in the fundamental equations of motion. Its nature is, rather, a statistical or probabilistic one, for it can be interpreted as a measure of the amount of chaos ...
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The maximum entropy principle in information retrieval
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '86, 1986Applications, assumptions and properties of the maximum entropy principle are discussed. The maximum entropy principle integrates prior estimates of relevance with the observed distribution of term combinations. The result may be a reordering of the segments of a database, compared to a naive estimate. Numerical examples obtained by solution of the non-
Paul B. Kantor, Jung Jin Lee
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The generalised maximum α entropy principle
International Journal of Mathematics in Operational Research, 2018Generalisations of maximum entropy principle (MEP) and minimum discrimination information principle (MDIP) are described by Kapur and Kesavan (1989) . In this paper, we used generalised entropies and replaced Shannon entropy with Tsallis entropy when α = 2 .
Manije Sanei Tabass +1 more
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