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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Veröffentlicht: MDPI - Multidisciplinary Digital Publishing Institute 2022
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description 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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spelling doab-20.500.12854ir-809582024-03-28T03:32:28Z The Statistical Foundations of Entropy Jizba, Petr Korbel, Jan ecological inference generalized cross entropy distributional weighted regression matrix adjustment entropy critical phenomena renormalization multiscale thermodynamics GENERIC non-Newtonian calculus non-Diophantine arithmetic Kolmogorov–Nagumo averages escort probabilities generalized entropies maximum entropy principle MaxEnt distribution calibration invariance Lagrange multipliers generalized Bilal distribution adaptive Type-II progressive hybrid censoring scheme maximum likelihood estimation Bayesian estimation Lindley’s approximation confidence interval Markov chain Monte Carlo method Rényi entropy Tsallis entropy entropic uncertainty relations quantum metrology non-equilibrium thermodynamics variational entropy rényi entropy tsallis entropy landsberg—vedral entropy gaussian entropy sharma—mittal entropy α-mutual information α-channel capacity maximum entropy Bayesian inference updating probabilities n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science 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. 2022-05-06T11:18:12Z 2022-05-06T11:18:12Z 2022 book ONIX_20220506_9783036535579_23 9783036535579 9783036535586 https://directory.doabooks.org/handle/20.500.12854/80958 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/5297 https://mdpi.com/books/pdfview/book/5297 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3558-6 10.3390/books978-3-0365-3558-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036535579 9783036535586 182 Basel open access
spellingShingle ecological inference
generalized cross entropy
distributional weighted regression
matrix adjustment
entropy
critical phenomena
renormalization
multiscale thermodynamics
GENERIC
non-Newtonian calculus
non-Diophantine arithmetic
Kolmogorov–Nagumo averages
escort probabilities
generalized entropies
maximum entropy principle
MaxEnt distribution
calibration invariance
Lagrange multipliers
generalized Bilal distribution
adaptive Type-II progressive hybrid censoring scheme
maximum likelihood estimation
Bayesian estimation
Lindley’s approximation
confidence interval
Markov chain Monte Carlo method
Rényi entropy
Tsallis entropy
entropic uncertainty relations
quantum metrology
non-equilibrium thermodynamics
variational entropy
rényi entropy
tsallis entropy
landsberg—vedral entropy
gaussian entropy
sharma—mittal entropy
α-mutual information
α-channel capacity
maximum entropy
Bayesian inference
updating probabilities
n/a
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::P Mathematics and Science
The Statistical Foundations of Entropy
title The Statistical Foundations of Entropy
title_full The Statistical Foundations of Entropy
title_fullStr The Statistical Foundations of Entropy
title_full_unstemmed The Statistical Foundations of Entropy
title_short The Statistical Foundations of Entropy
title_sort statistical foundations of entropy
topic ecological inference
generalized cross entropy
distributional weighted regression
matrix adjustment
entropy
critical phenomena
renormalization
multiscale thermodynamics
GENERIC
non-Newtonian calculus
non-Diophantine arithmetic
Kolmogorov–Nagumo averages
escort probabilities
generalized entropies
maximum entropy principle
MaxEnt distribution
calibration invariance
Lagrange multipliers
generalized Bilal distribution
adaptive Type-II progressive hybrid censoring scheme
maximum likelihood estimation
Bayesian estimation
Lindley’s approximation
confidence interval
Markov chain Monte Carlo method
Rényi entropy
Tsallis entropy
entropic uncertainty relations
quantum metrology
non-equilibrium thermodynamics
variational entropy
rényi entropy
tsallis entropy
landsberg—vedral entropy
gaussian entropy
sharma—mittal entropy
α-mutual information
α-channel capacity
maximum entropy
Bayesian inference
updating probabilities
n/a
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::P Mathematics and Science
topic_facet ecological inference
generalized cross entropy
distributional weighted regression
matrix adjustment
entropy
critical phenomena
renormalization
multiscale thermodynamics
GENERIC
non-Newtonian calculus
non-Diophantine arithmetic
Kolmogorov–Nagumo averages
escort probabilities
generalized entropies
maximum entropy principle
MaxEnt distribution
calibration invariance
Lagrange multipliers
generalized Bilal distribution
adaptive Type-II progressive hybrid censoring scheme
maximum likelihood estimation
Bayesian estimation
Lindley’s approximation
confidence interval
Markov chain Monte Carlo method
Rényi entropy
Tsallis entropy
entropic uncertainty relations
quantum metrology
non-equilibrium thermodynamics
variational entropy
rényi entropy
tsallis entropy
landsberg—vedral entropy
gaussian entropy
sharma—mittal entropy
α-mutual information
α-channel capacity
maximum entropy
Bayesian inference
updating probabilities
n/a
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
thema EDItEUR::P Mathematics and Science
url ONIX_20220506_9783036535579_23