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Evidence theory is that the extension of probability can better deal with unknowns and inaccurate information. Uncertainty measurement plays a vital role in both evidence theory and probability theory. Approximate Entropy (ApEn) is proposed…

Artificial Intelligence · Computer Science 2021-05-19 Tianxiang Zhan , Yuanpeng He , Hanwen Li , Fuyuan Xiao

Consider a state of a system with several subsystems. The entropies of the reduced state on different subsystems obey certain inequalities, provided there is an equivalence relation, and a function measuring volumes or weights of…

Mathematical Physics · Physics 2009-11-07 Bernhard Baumgartner

We study the stability of deterministic systems given sequences of large, jump-like perturbations. Our main result is to dervie a lower bound for the probability of the system to remain in the basin, given that perturbations are rare…

Chaotic Dynamics · Physics 2019-11-26 Paul Schultz , Frank Hellmann , Kevin N. Webster , Jürgen Kurths

The paper describes an approach to measuring convergence of an algorithm to its result in terms of an entropy-like function of partitions of its inputs of a given length. The goal is to look at the algorithmic data processing from the…

Computational Complexity · Computer Science 2016-05-06 Anatol Slissenko

The postulational basis of classical thermodynamics has been expanded to incorporate equilibrium fluctuations. The main additional elements of the proposed thermodynamic theory are the concept of quasi-equilibrium states, a definition of…

Statistical Mechanics · Physics 2019-06-11 Y. Mishin

Entropic uncertainty relations, based on sums of entropies of probability distributions arising from different measurements on a given pure state, can be seen as a generalization of the Heisenberg uncertainty relation that is in many cases…

Quantum Physics · Physics 2007-05-23 Adam Azarchs

In the real world, one almost never knows the parameters of a thermodynamic process to infinite precision. Reflecting this, here we investigate how to extend stochastic thermodynamics to systems with uncertain parameters, including…

Statistical Mechanics · Physics 2021-03-17 Jan Korbel , David H. Wolpert

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

Observational entropy provides a general notion of quantum entropy that appropriately interpolates between Boltzmann's and Gibbs' entropies, and has recently been argued to provide a useful measure of out-of-equilibrium thermodynamic…

Quantum Physics · Physics 2023-05-09 Francesco Buscemi , Joseph Schindler , Dominik Šafránek

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We firstly define a purely structural…

Disordered Systems and Neural Networks · Physics 2013-05-30 James West , Lucas Lacasa , Simone Severini , Andrew Teschendorff

The population dynamics and stability of ecosystems of interacting species is studied from the perspective of non-equilibrium thermodynamics by assuming that species, through their biotic and abiotic interactions, are units of entropy…

Biological Physics · Physics 2007-05-23 Karo Michaelian

In the last three decades, several measures of complexity have been proposed. Up to this point, most of such measures have only been developed for finite spaces. In these scenarios the baseline distribution is uniform. This makes sense…

Information Theory · Computer Science 2021-11-15 Daniel Andrés Díaz-Pachón , Robert J. Marks

The use of maximum entropy inference in reasoning with uncertain information is commonly justified by an information-theoretic argument. This paper discusses a possible objection to this information-theoretic justification and shows how it…

Artificial Intelligence · Computer Science 2013-04-15 Daniel Hunter

Current research in statistical mechanics mostly concerns the investigation of out-of-equilibrium, irreversible processes, which are ubiquitous in nature and still far from being theoretically understood. Even the precise characterization…

The study of complex systems has attracted widespread attention from researchers in the fields of natural sciences, social sciences, and engineering. Prediction is one of the central issues in this field. Although most related studies have…

Physics and Society · Physics 2025-10-21 En Xu , Yilin Bi , Hongwei Hu , Xin Chen , Zhiwen Yu , Yong Li , Yanqing Hu , Tao Zhou

We introduce an ambidextrous view of stochastic dynamical systems, comparing their forward-time and reverse-time representations and then integrating them into a single time-symmetric representation. The perspective is useful theoretically,…

Statistical Mechanics · Physics 2015-05-13 Christopher J. Ellison , John R. Mahoney , James P. Crutchfield

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

The rate of entropy production by a stochastic process quantifies how far it is from thermodynamic equilibrium. Equivalently, entropy production captures the degree to which detailed balance and time-reversal symmetry are broken. Despite…

Statistical Mechanics · Physics 2020-12-02 Luca Cocconi , Rosalba Garcia-Millan , Zigan Zhen , Bianca Buturca , Gunnar Pruessner

The climate system is a forced, dissipative, nonlinear, complex and heterogeneous system that is out of thermodynamic equilibrium. The system exhibits natural variability on many scales of motion, in time as well as space, and it is subject…

Atmospheric and Oceanic Physics · Physics 2020-08-05 Michael Ghil , Valerio Lucarini

Symbolic dynamics has proven to be an invaluable tool in analyzing the mechanisms that lead to unpredictability and random behavior in nonlinear dynamical systems. Surprisingly, a discrete partition of continuous state space can produce a…

Machine Learning · Computer Science 2007-07-13 Christopher C. Strelioff , James P. Crutchfield