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The problem of assigning probability distributions which objectively reflect the prior information available about experiments is one of the major stumbling blocks in the use of Bayesian methods of data analysis. In this paper the method of…

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

In most data-scientific approaches, the principle of Maximum Entropy (MaxEnt) is used to a posteriori justify some parametric model which has been already chosen based on experience, prior knowledge or computational simplicity. In a…

Methodology · Statistics 2022-06-29 Orestis Loukas , Ho Ryun Chung

The superstatistics concept is a useful statistical method to describe inhomogeneous complex systems for which a system parameter $\beta$ fluctuates on a large spatio-temporal scale. In this paper we analyze a measured time series of wind…

Statistical Mechanics · Physics 2010-12-22 Erik Van der Straeten , Christian Beck

Jaynes' information theory formalism of statistical mechanics is applied to the stationary states of open, non-equilibrium systems. The key result is the construction of the probability distribution for the underlying microscopic phase…

Statistical Mechanics · Physics 2009-10-31 Roderick C. Dewar

To describe the nonequilibrium states of a system we introduce a new thermodynamic parameter - the lifetime of a system. The statistical distributions which can be obtained out of the mesoscopic description characterizing the behaviour of a…

Statistical Mechanics · Physics 2007-05-23 V. V. Ryazanov , S. G. Shpyrko

Numerical methods for the description of nonequilibrium many-particle quantum systems such as equation of motion techniques often cannot compute the full statistics of observables but only moments of it, such as mean, variance and…

Statistical Mechanics · Physics 2018-12-05 Boris Gulyak , Boris Melcher , Jan Wiersig

We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers [10] and [4] who proposed the estimation of parameters where…

Neurons and Cognition · Quantitative Biology 2015-06-19 Hassan Nasser , Bruno Cessac

The nonextensive statistical ensembles are revisited for the complex systems with long-range interactions and long-range correlations. An approximation, the value of nonextensive parameter (1-q) is assumed to be very tiny, is adopted for…

Statistical Mechanics · Physics 2020-02-26 Yahui Zheng , Jiulin Du , Linxia Liu , Huijun Kong

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

The selection of an equilibrium state by maximising the entropy of a system, subject to certain constraints, is often powerfully motivated as an exercise in logical inference, a procedure where conclusions are reached on the basis of…

Statistical Mechanics · Physics 2015-12-03 Ian J. Ford

Nonequilibrium systems with large-scale fluctuations of a suitable system parameter are often effectively described by a superposition of two statistics, a superstatistics. Here we illustrate this concept by analysing experimental data of…

Statistical Mechanics · Physics 2009-11-11 C. Beck , E. G. D. Cohen , S. Rizzo

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

A generic feature of systems with long-range interactions is the presence of {\it quasi-stationary} states with non-Gaussian single particle velocity distributions. For the case of the Hamiltonian Mean Field (HMF) model, we demonstrate that…

We introduce a finite-time detailed fluctuation theorem for the environmental entropy of the form $\tilde P(\Delta S_{env}) = e^{\Delta S_{env}} \tilde P(-\Delta S_{env})$ for an appropriately weighted probability density of the external…

Statistical Mechanics · Physics 2013-04-22 David Luposchainsky , Andre Cardoso Barato , Haye Hinrichsen

A theory for non-equilibrium systems is derived from a maximum entropy approach similar in spirit to the equilibrium theory given by Gibbs. Requiring Hamilton's principle of stationary action to be satisfied on average during a trajectory,…

Statistical Mechanics · Physics 2019-02-04 David M. Rogers , Susan B. Rempe

We study the distribution of maxima (Extreme Value Statistics) for sequences of observables computed along orbits generated by random transformations. The underlying, deterministic, dynamical system can be regular or chaotic. In the former…

Dynamical Systems · Mathematics 2015-06-11 Davide Faranda , Jorge Milhazes Freitas , Valerio Lucarini , Giorgio Turchetti , Sandro Vaienti

The paper analyzes the entropy of a system composed by non-interacting and indistinguishable particles whose quantum state numbers are modelled as independent and identically distributed classical random variables. The crucial observation…

Statistical Mechanics · Physics 2023-05-18 Arnaldo Spalvieri

By assuming an appropriate energy composition law between two systems governed by the same non-extensive entropy, we revisit the definitions of temperature and pressure, arising from the zeroth principle of thermodynamics, in a manner…

Statistical Mechanics · Physics 2015-05-18 A. M. Scarfone

A common statistical situation concerns inferring an unknown distribution Q(x) from a known distribution P(y), where X (dimension n), and Y (dimension m) have a known functional relationship. Most commonly, n<m, and the task is relatively…

Quantitative Methods · Quantitative Biology 2016-02-01 Jayajit Das , Sayak Mukherjee , Susan E. Hodge

The maximum entropy principle from statistical mechanics states that a closed system attains an equilibrium distribution that maximizes its entropy. We first show that for graphs with fixed number of edges one can define a stochastic edge…

Disordered Systems and Neural Networks · Physics 2007-05-23 Jesse S. A. Bridgewater , P. Oscar Boykin , Vwani P. Roychowdhury
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