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Statistical properties of coupled dynamic-stochastic systems are studied within a combination of the maximum information principle and the superstatistical approach. The conditions at which the Shannon entropy functional leads to a…

Statistical Mechanics · Physics 2009-11-11 E. V. Vakarin , J. P. Badiali

We develop a new theoretical framework for describing steady-state quantum transport phenomena, based on the general maximum-entropy principle of non-equilibrium statistical mechanics. The general form of the many-body density matrix is…

Mesoscale and Nanoscale Physics · Physics 2007-05-23 P. Bokes , R. W. Godby

The entropy maximum approach (Maxent) was developed as a minimization of the subjective uncertainty measured by the Boltzmann--Gibbs--Shannon entropy. Many new entropies have been invented in the second half of the 20th century. Now there…

Data Analysis, Statistics and Probability · Physics 2013-11-07 A. N. Gorban

Depending on context, the term entropy is used for a thermodynamic quantity, a~measure of available choice, a quantity to measure information, or, in the context of statistical inference, a maximum configuration predictor. For systems in…

Statistical Mechanics · Physics 2018-11-14 Rudolf Hanel , Stefan Thurner

A principle of maximum entropy is proposed in the context of viscous incompressible flow in Eulerian coordinates. The relative entropy functional, defined over the space of $L^2$ divergence-free velocity fields, is maximized relative to…

Fluid Dynamics · Physics 2024-02-23 Gui-Qiang G. Chen , James Glimm , Hamid Said

The method of Maximum (relative) Entropy (ME) is used to translate the information contained in the known form of the likelihood into a prior distribution for Bayesian inference. The argument is guided by intuition gained from the…

Data Analysis, Statistics and Probability · Physics 2009-11-10 Ariel Caticha , Roland Preuss

We show that the naive application of the maximum entropy principle can yield answers which depend on the level of description, i.e. the result is not invariant under coarse-graining. We demonstrate that the correct approach, even for…

Statistical Mechanics · Physics 2007-05-23 Jayanth Banavar , Amos Maritan

A brief discussion is given of the traditional version of the Maximum Entropy Method, including a review of some of the criticism that has been made in regard to its use in statistical inference. Motivated by these questions, a modified…

Data Analysis, Statistics and Probability · Physics 2007-09-12 Robert Kariotis

The well known maximum-entropy principle due to Jaynes, which states that given mean parameters, the maximum entropy distribution matching them is in an exponential family, has been very popular in machine learning due to its "Occam's…

Machine Learning · Computer Science 2016-07-13 Yuanzhi Li , Andrej Risteski

The maximum entropy technique (MENT) is used to determine the distribution functions of physical values. MENT naturally combines required maximum entropy, the properties of a system and connection conditions in the form of restrictions…

High Energy Physics - Experiment · Physics 2007-05-23 B. Z. Belashev , M. K. Suleymanov

A novel formalism, called H-theory, is applied to the problem of statistical equilibrium of a hierarchical complex system with multiple time and length scales. In this approach, the system is formally treated as being composed of a small…

Statistical Mechanics · Physics 2019-05-06 Giovani L. Vasconcelos , Domingos S. P. Salazar , A. M. S. Macêdo

Maximum entropy models are increasingly being used to describe the collective activity of neural populations with measured mean neural activities and pairwise correlations, but the full space of probability distributions consistent with…

Biological Physics · Physics 2017-08-22 Badr F. Albanna , Christopher Hillar , Jascha Sohl-Dickstein , Michael R. DeWeese

Nonequilibrium complex systems are often effectively described by the mixture of different dynamics on different time scales. Superstatistics, which is "statistics of statistics" with two largely separated time scales, offers a consistent…

Statistical Mechanics · Physics 2015-05-18 Sumiyoshi Abe

The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible while constrained to match empirically estimated feature expectations. However, in many real-world…

Machine Learning · Computer Science 2022-08-16 Kenneth Bogert , Yikang Gui , Prashant Doshi

Entropy maximization procedure has been a general practice in many diverse fields of science to obtain the concomitant probability distributions. The consistent use of the maximization procedure on the other hand requires the probability…

Statistical Mechanics · Physics 2018-10-17 Thomas Oikonomou , G. Baris Bagci

Based on the q-exponential distribution which has been observed in more and more physical systems, the varentropy method is used to derive the uncertainty measure of such an abnormal distribution function. The uncertainty measure obtained…

Mathematical Physics · Physics 2010-09-07 Congjie Ou , Aziz El Kaabouchi , Qiuping A. Wang , Jincan Chen

A new concept named nonsymmetric entropy which generalizes the concepts of Boltzman's entropy and shannon's entropy, was introduced. Maximal nonsymmetric entropy principle was proven. Some important distribution laws were derived naturally…

Information Theory · Computer Science 2007-07-13 Chengshi Liu

Dissipative quantum systems are frequently described within the framework of the so-called "system-plus-reservoir" approach. In this work we assign their description to the Maximum Entropy Formalism and compare the resulting thermodynamic…

Statistical Mechanics · Physics 2017-01-04 Lisan M. M. Durão , Amir O. Caldeira

A path information is defined in connection with different possible paths of irregular dynamic systems moving in its phase space between two points. On the basis of the assumption that the paths are physically differentiated by their…

Statistical Mechanics · Physics 2007-05-23 Qiuping A. Wang

For the purpose of causal inference we employ a stochastic model of the data generating process, utilizing individual propensity probabilities for the treatment, and also individual and counterfactual prognosis probabilities for the…

Methodology · Statistics 2024-07-15 Brian Knaeble , Mehdi Hakim-Hashemi , Mark A. Abramson