Non-Extensive Entropic Distance Based on Diffusion: Restrictions on Parameters in Entropy Formulae

Bı́ró, Tamás; Schram, Zsolt

doi:10.3390/e18020042

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…The change of the entropic distance is governed by its definition and the evolution equation for the distribution. The entropic distance of an actual, time-dependent distribution, P n (t), to the stationary distribution, Q n has the trace form [40]:…”

Section: Rates Leading To Rates Leading To Gamma Distribution Gamma Dmentioning

confidence: 99%

Dynamical stationarity as a result of sustained random growth

Bı́ró

Néda

2017

Phys. Rev. E

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In sustained growth with random dynamics stationary distributions can exist without detailed balance. This suggests thermodynamical behavior in fast-growing complex systems. In order to model such phenomena we apply both a discrete and a continuous master equation. The derivation of elementary rates from known stationary distributions is a generalization of the fluctuation-dissipation theorem. Entropic distance evolution is given for such systems. We reconstruct distributions obtained for growing networks, particle production, scientific citations, and income distribution.

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Section: Rates Leading To Rates Leading To Gamma Distribution Gamma Dmentioning

confidence: 99%

Dynamical stationarity as a result of sustained random growth

Bı́ró

Néda

2017

Phys. Rev. E

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“…Using further assumptions about reservoir fluctuations, further entropy formulas can be constructed, as expectation values of formal logarithms, behaving additively [194].…”

Section: Physical Sources Of Nb Statesmentioning

confidence: 99%

Nuclear and quark matter at high temperature

2017

Self Cite

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Abstract.We review important ideas on nuclear and quark matter description on the basis of hightemperature field theory concepts, like resummation, dimensional reduction, interaction scale separation and spectral function modification in media. Statistical and thermodynamical concepts are spotted in the light of these methods concentrating on the -partially still open-problems of the hadronization process.

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“…The conditional entropy of two variables X and Y taking values x and y , respectively, is defined by:

where b is the logarithm base. ED has the following properties [ 21 ]: ED is symmetric. ED is zero for comparing a distribution with itself.…”

Section: Simulation Modelsmentioning

confidence: 99%

Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions

Kachouie

Deebani

2020

Entropy

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Background: In data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson’s correlation coefficient has been widely used, its value is reliable only for linear relationships and Distance correlation was introduced to address this shortcoming. Methods: Distance correlation can identify linear and nonlinear correlations. However, its performance drops in noisy conditions. In this paper, we introduce the Association Factor (AF) as a robust method for identification and quantification of linear and nonlinear associations in noisy conditions. Results: To test the performance of the proposed Association Factor, we modeled several simulations of linear and nonlinear relationships in different noise conditions and computed Pearson’s correlation, Distance correlation, and the proposed Association Factor. Conclusion: Our results show that the proposed method is robust in two ways. First, it can identify both linear and nonlinear associations. Second, the proposed Association Factor is reliable in both noiseless and noisy conditions.

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Non-Extensive Entropic Distance Based on Diffusion: Restrictions on Parameters in Entropy Formulae

Cited by 4 publications

References 38 publications

Dynamical stationarity as a result of sustained random growth

Dynamical stationarity as a result of sustained random growth

Nuclear and quark matter at high temperature

Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions

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