Strictly and asymptotically scale-invariant probabilistic models of $N$ correlated binary random variables having {\em q}--Gaussians as $N\to \infty$ limiting distributions
A. Rodr\'iguez, V. Schw\"ammle, C. Tsallis

TL;DR
This paper explores scale-invariant probabilistic models of correlated binary variables, showing that certain models converge to q-Gaussians as the number of variables grows, and clarifying the relationship between scale-invariance and q-Gaussian attractors.
Contribution
It introduces new classes of scale-invariant models and analytically and numerically investigates their limiting distributions, linking scale-invariance to q-Gaussian behavior.
Findings
Models (i) converge to q-Gaussians with specific q-values.
Models (ii) approach q-Gaussians asymptotically.
Models (iii) do not necessarily approach q-Gaussians despite scale-invariance.
Abstract
In order to physically enlighten the relationship between {\it --independence} and {\it scale-invariance}, we introduce three types of asymptotically scale-invariant probabilistic models with binary random variables, namely (i) a family, characterized by an index , unifying the Leibnitz triangle () and the case of independent variables (); (ii) two slightly different discretizations of --Gaussians; (iii) a special family, characterized by the parameter , which generalizes the usual case of independent variables (recovered for ). Models (i) and (iii) are in fact strictly scale-invariant. For models (i), we analytically show that the probability distribution is a --Gaussian with . Models (ii) approach --Gaussians by construction, and we numerically show that they do so with asymptotic…
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