A maximum-entropy principle for two-dimensional perfect fluid dynamics

1991 ◽  
Vol 65 (3-4) ◽  
pp. 531-553 ◽  
Author(s):  
Raoul Robert
Keyword(s):  
Fluid Dynamics ◽  
Maximum Entropy ◽  
Perfect Fluid ◽  
Maximum Entropy Principle ◽  
Two Dimensional ◽  
Entropy Principle

scholarly journals Probabilistic Inference for Dynamical Systems

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 696 ◽  
Author(s):  
Sergio Davis ◽  
Diego González ◽  
Gonzalo Gutiérrez
Keyword(s):  
Fluid Dynamics ◽  
Probability Theory ◽  
Dynamical Systems ◽  
Maximum Entropy ◽  
General Framework ◽  
Continuity Equation ◽  
Maximum Entropy Principle ◽  
Bayesian Probability ◽  
Specific Information ◽  
Entropy Principle

A general framework for inference in dynamical systems is described, based on the language of Bayesian probability theory and making use of the maximum entropy principle. Taking the concept of a path as fundamental, the continuity equation and Cauchy’s equation for fluid dynamics arise naturally, while the specific information about the system can be included using the maximum caliber (or maximum path entropy) principle.

scholarly journals Probabilistic Inference for Dynamical Systems

2018 ◽  
Author(s):  
Sergio Davis ◽  
Diego González ◽  
Gonzalo Gutiérrez
Keyword(s):  
Fluid Dynamics ◽  
Probability Theory ◽  
Dynamical Systems ◽  
Maximum Entropy ◽  
General Framework ◽  
Continuity Equation ◽  
Maximum Entropy Principle ◽  
Bayesian Probability ◽  
Specific Information ◽  
Entropy Principle

A general framework for inference in dynamical systems is described, based on the language of Bayesian probability theory and making use of the maximum entropy principle. Taking as fundamental the concept of a path, the continuity equation and Cauchy's equation for fluid dynamics arise naturally, while the specific information about the system can be included using the Maximum Caliber (or maximum path entropy) principle.

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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