A generalization of the central limit theorem consistent with nonextensive statistical mechanics
arXiv:cond-mat/0603593 · doi:10.1063/1.2828756
Abstract
The standard central limit theorem plays a fundamental role in Boltzmann-Gibbs statistical mechanics. This important physical theory has been generalized \cite{Tsallis1988} in 1988 by using the entropy (with ) instead of its particular BG case . The theory which emerges is usually referred to as {\it nonextensive statistical mechanics} and recovers the standard theory for . During the last two decades, this -generalized statistical mechanics has been successfully applied to a considerable amount of physically interesting complex phenomena. A conjecture\cite{Tsallis2005} and numerical indications available in the literature have been, for a few years, suggesting the possibility of -versions of the standard central limit theorem by allowing the random variables that are being summed to be strongly correlated in some special manner, the case corresponding to standard probabilistic independence. This is what we prove in the present paper for . The attractor, in the usual sense of a central limit theorem, is given by a distribution of the form with , and normalizing constant . These distributions, sometimes referred to as -Gaussians, are known to make, under appropriate constraints, extremal the functional (in its continuous version). Their and particular cases recover respectively Gaussian and Cauchy distributions.
19 pages (the new version contains further simplifications and precisions with regard to the previous one)
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Cited by in corpus (22)
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- Strictly and asymptotically scale-invariant probabilistic models of correlated binary random variables having {\em q}--Gaussians as limiting distributions
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- Nonextensive statistical mechanics and central limit theorems I - Convolution of independent random variables and q-product
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- On a representation of the inverse Fq transform
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- Multivariate Generalizations of the q--Central Limit Theorem
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