scholarly journals Maximum entropy principle explains quasistationary states in systems with long-range interactions: The example of the Hamiltonian mean-field model

2007 ◽  
Vol 75 (1) ◽  
Author(s):  
Andrea Antoniazzi ◽  
Duccio Fanelli ◽  
Julien Barré ◽  
Pierre-Henri Chavanis ◽  
Thierry Dauxois ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Long Range ◽  
Field Model ◽  
Mean Field ◽  
Maximum Entropy Principle ◽  
Mean Field Model ◽  
Long Range Interactions ◽  
Entropy Principle

scholarly journals Quark mean-field model for single and double   and   hypernuclei

2014 ◽  
Vol 2014 (1) ◽  
pp. 13D02-0 ◽  
Author(s):  
J. N. Hu ◽  
A. Li ◽  
H. Shen ◽  
H. Toki
Keyword(s):  
Field Model ◽  
Mean Field ◽  
Mean Field Model

EVOLUTION OF SHELL STRUCTURE IN NUCLEI

2011 ◽  
Vol 20 (08) ◽  
pp. 1663-1675 ◽  
Author(s):  
A. BHAGWAT ◽  
Y. K. GAMBHIR
Keyword(s):  
Shell Structure ◽  
Crucial Role ◽  
Field Model ◽  
Mean Field ◽  
Mass Region ◽  
The Other ◽  
Mean Field Model ◽  
Relativistic Mean Field ◽  
Shell Closures ◽  
Low Mass

Systematic investigations of the pairing and two-neutron separation energies which play a crucial role in the evolution of shell structure in nuclei, are carried out within the framework of relativistic mean-field model. The shell closures are found to be robust, as expected, up to the lead region. New shell closures appear in low mass region. In the superheavy region, on the other hand, it is found that the shell closures are not as robust, and they depend on the particular combinations of neutron and proton numbers. Effect of deformation on the shell structure is found to be marginal.

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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