Probabilistic Models with Nonlocal Correlations: Numerical Evidence of q-Large Deviation Theory
Dario Javier Zamora, Constantino Tsallis

TL;DR
This paper introduces a family of correlated probabilistic models that generalize previous nonextensive statistical mechanics models, providing numerical evidence supporting a $q$-generalized Large Deviation Theory and deepening understanding of complex systems with global correlations.
Contribution
It generalizes a previous model to produce a broader family of $Q$-Gaussian attractors and supports a $q$-generalized Large Deviation Theory through numerical analysis.
Findings
Family of models yields $Q_c$-Gaussians in the large N limit.
Numerical evidence supports $q$-generalized Large Deviation Theory.
Models are isomorphic via a monotonic transformation Q_c(Q).
Abstract
The correlated probabilistic model introduced and analytically discussed in Hanel et al (2009) is based on a self-dual transformation of the index which characterizes a current generalization of Boltzmann-Gibbs statistical mechanics, namely nonextensive statistical mechanics, and yields, in the limit, a -Gaussian distribution for any chosen value of . We show here that, by properly generalizing that self-dual transformation, it is possible to obtain an entire family of such probabilistic models, all of them yielding -Gaussians () in the limit. This family turns out to be isomorphic to the Hanel et al model through a specific monotonic transformation . Then, by following along the lines of Tirnakli et al (2022), we numerically show that this family of correlated probabilistic models provides further evidence towards a…
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Taxonomy
TopicsStatistical Mechanics and Entropy · Complex Systems and Time Series Analysis · Theoretical and Computational Physics
