Determination of the Surface Elevation Probability Distribution of Wind Waves Using Maximum Entropy Principle

1990 ◽  
pp. 345-348
Author(s):  
Witold Cieślikiewicz
Keyword(s):  
Probability Distribution ◽  
Maximum Entropy ◽  
Wind Waves ◽  
Maximum Entropy Principle ◽  
Surface Elevation ◽  
Entropy Principle

scholarly journals A LINK BETWEEN THE MAXIMUM ENTROPY APPROACH AND THE VARIATIONAL ENTROPY FORM

2011 ◽  
Vol 25 (22) ◽  
pp. 1821-1828 ◽  
Author(s):  
E. V. VAKARIN ◽  
J. P. BADIALI
Keyword(s):  
Probability Distribution ◽  
Maximum Entropy ◽  
Variational Formulation ◽  
Variational Approach ◽  
Maximum Entropy Principle ◽  
Random Variable ◽  
Entropy Measure ◽  
Universal Relation ◽  
Amount Of Information ◽  
Entropy Principle

The maximum entropy approach operating with quite general entropy measure and constraint is considered. It is demonstrated that for a conditional or parametrized probability distribution f(x|μ), there is a "universal" relation among the entropy rate and the functions appearing in the constraint. This relation allows one to translate the specificities of the observed behavior θ(μ) into the amount of information on the relevant random variable x at different values of the parameter μ. It is shown that the recently proposed variational formulation of the entropic functional can be obtained as a consequence of this relation, that is from the maximum entropy principle. This resolves certain puzzling points that appeared in the variational approach.

scholarly journals Identifying the Probability Distribution of Fatigue Life Using the Maximum Entropy Principle

Entropy ◽  
2016 ◽  
Vol 18 (4) ◽  
pp. 111 ◽  
Author(s):  
Hongshuang Li ◽  
Debing Wen ◽  
Zizi Lu ◽  
Yu Wang ◽  
Feng Deng
Keyword(s):  
Fatigue Life ◽  
Probability Distribution ◽  
Maximum Entropy ◽  
Maximum Entropy Principle ◽  
Entropy Principle

Use in Probabilistic Design of the Maximum Entrophy Distribution Based on Ranked Data

1980 ◽  
Vol 102 (3) ◽  
pp. 460-468
Author(s):  
J. N. Siddall ◽  
Ali Badawy
Keyword(s):  
Probability Distribution ◽  
Maximum Entropy ◽  
Probability Distributions ◽  
Maximum Entropy Principle ◽  
Random Variable ◽  
Probabilistic Design ◽  
Entropy Principle ◽  
Almost All ◽  
New Distribution

A new algorithm using the maximum entropy principle is introduced to estimate the probability distribution of a random variable, using directly a ranked sample. It is demonstrated that almost all of the analytical probability distributions can be approximated by the new algorithm. A comparison is made between existing methods and the new algorithm; and examples are given of fitting the new distribution to an actual ranked sample.

Natural partitioning of orientation elements and determination of the ODF from individual orientations according to the maximum-entropy principle

1999 ◽  
Vol 32 (3) ◽  
pp. 404-408
Author(s):  
Y. D. Wang ◽  
A. Vadon ◽  
J. Bessières ◽  
J. J. Heizmann
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Maximum Entropy Principle ◽  
Orientation Distribution ◽  
Entropy Method ◽  
Euler Space ◽  
Entropy Principle ◽  
Orientation Density ◽  
Preliminary Orientation

The orientation distribution function (ODF) of a polycrystalline material is usually constructed from individual orientations by the harmonic method on the assumption of a certain function distribution in the Euler space around each orientation. In the present paper, a new method is developed to determine the ODF from individual orientations. A natural partitioning of the orientation elements in the Euler space around some clustered orientations is proposed. Thus, the preliminary values of orientation density in the elements are directly estimated by the volumes of the orientation elements and the number of grains (or measured points) in each orientation element. Then, the texture vector is further refined using the maximum-entropy method with the preliminary orientation densities as constraints. The validity of this method is exemplified by the texture analysis of a cubic material from individual orientations modelled by Gaussian distribution.

An entropy concentration theorem: applications in artificial intelligence and descriptive statistics

1990 ◽  
Vol 27 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Claudine Robert
Keyword(s):  
Artificial Intelligence ◽  
Maximum Entropy ◽  
Large Deviation ◽  
Maximum Entropy Principle ◽  
Statistical Modelling ◽  
Linear Constraints ◽  
Descriptive Statistics ◽  
Entropy Principle ◽  
Maximum Entropy Distribution ◽  
Mathematical Justification

The maximum entropy principle is used to model uncertainty by a maximum entropy distribution, subject to some appropriate linear constraints. We give an entropy concentration theorem (whose demonstration is based on large deviation techniques) which is a mathematical justification of this statistical modelling principle. Then we indicate how it can be used in artificial intelligence, and how relevant prior knowledge is provided by some classical descriptive statistical methods. It appears furthermore that the maximum entropy principle yields to a natural binding between descriptive methods and some statistical structures.

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