The Statistical Foundations of Entropy

In the last two decades, the understanding of complex dynamical systems underwent important conceptual shifts. The catalyst was the infusion of new ideas from the theory of critical phenomena (scaling laws, renormalization group, etc.), (multi)fractals and trees, random matrix theory, network theory...

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Year of Publication:2022
Language:English
Physical Description:1 electronic resource (182 p.)
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520 |a In the last two decades, the understanding of complex dynamical systems underwent important conceptual shifts. The catalyst was the infusion of new ideas from the theory of critical phenomena (scaling laws, renormalization group, etc.), (multi)fractals and trees, random matrix theory, network theory, and non-Shannonian information theory. The usual Boltzmann–Gibbs statistics were proven to be grossly inadequate in this context. While successful in describing stationary systems characterized by ergodicity or metric transitivity, Boltzmann–Gibbs statistics fail to reproduce the complex statistical behavior of many real-world systems in biology, astrophysics, geology, and the economic and social sciences.The aim of this Special Issue was to extend the state of the art by original contributions that could contribute to an ongoing discussion on the statistical foundations of entropy, with a particular emphasis on non-conventional entropies that go significantly beyond Boltzmann, Gibbs, and Shannon paradigms. The accepted contributions addressed various aspects including information theoretic, thermodynamic and quantum aspects of complex systems and found several important applications of generalized entropies in various systems. 
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653 |a distributional weighted regression 
653 |a matrix adjustment 
653 |a entropy 
653 |a critical phenomena 
653 |a renormalization 
653 |a multiscale thermodynamics 
653 |a GENERIC 
653 |a non-Newtonian calculus 
653 |a non-Diophantine arithmetic 
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653 |a generalized entropies 
653 |a maximum entropy principle 
653 |a MaxEnt distribution 
653 |a calibration invariance 
653 |a Lagrange multipliers 
653 |a generalized Bilal distribution 
653 |a adaptive Type-II progressive hybrid censoring scheme 
653 |a maximum likelihood estimation 
653 |a Bayesian estimation 
653 |a Lindley's approximation 
653 |a confidence interval 
653 |a Markov chain Monte Carlo method 
653 |a Rényi entropy 
653 |a Tsallis entropy 
653 |a entropic uncertainty relations 
653 |a quantum metrology 
653 |a non-equilibrium thermodynamics 
653 |a variational entropy 
653 |a rényi entropy 
653 |a tsallis entropy 
653 |a landsberg-vedral entropy 
653 |a gaussian entropy 
653 |a sharma-mittal entropy 
653 |a α-mutual information 
653 |a α-channel capacity 
653 |a maximum entropy 
653 |a Bayesian inference 
653 |a updating probabilities 
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