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.

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 Inferences from a network to a subnetwork and vice versa under an assumption of symmetry

2015 ◽  
Author(s):  
PierGianLuca Porta Mana ◽  
Emiliano Torre ◽  
Vahid Rostami
Keyword(s):  
Probability Theory ◽  
Maximum Entropy ◽  
Probability Distributions ◽  
Maximum Entropy Principle ◽  
Entropy Principle ◽  
Mathematical Relations

This note summarizes some mathematical relations between the probability distributions for the states of a network of binary units and a subnetwork thereof, under an assumption of symmetry. These relations are standard results of probability theory, but seem to be rarely used in neuroscience. Some of their consequences for inferences between network and subnetwork, especially in connection with the maximum-entropy principle, are briefly discussed. The meanings and applicability of the assumption of symmetry are also discussed.

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
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