English

Emergence of $q$-statistical functions in a generalized binomial distribution with strong correlations

Statistical Mechanics 2015-05-20 v1

Abstract

We study a symmetric generalization pk(N)(η,α)\mathfrak{p}^{(N)}_k(\eta, \alpha) of the binomial distribution recently introduced by Bergeron et al, where η[0,1]\eta \in [0,1] denotes the win probability, and α\alpha is a positive parameter. This generalization is based on qq-exponential generating functions (eqgenz[1+(1qgen)z]1/(1qgen);e1z=ez)e_{q^{gen}}^z \equiv [1+(1-q^{gen})z]^{1/(1-q^{gen})};\,e_{1}^z=e^z) where qgen=1+1/αq^{gen}=1+1/\alpha. The numerical calculation of the probability distribution function of the number of wins kk, related to the number of realizations NN, strongly approaches a discrete qdiscq^{disc}-Gaussian distribution, for win-loss equiprobability (i.e., η=1/2\eta=1/2) and all values of α\alpha. Asymptotic NN\to \infty distribution is in fact a qattq^{att}-Gaussian eqattβz2e_{q^{att}}^{-\beta z^2}, where qatt=12/(α2)q^{att}=1-2/(\alpha-2) and β=(2α4)\beta=(2\alpha-4). The behavior of the scaled quantity k/Nγk/N^\gamma is discussed as well. For γ<1\gamma<1, a large-deviation-like property showing a qldlq^{ldl}-exponential decay is found, where qldl=1+1/(ηα)q^{ldl}=1+1/(\eta\alpha). For η=1/2\eta=1/2, qldlq^{ldl} and qattq^{att} are related through 1/(qldl1)+1/(qatt1)=11/(q^{ldl}-1)+1/(q^{att}-1)=1, α\forall \alpha. For γ=1\gamma=1, the law of large numbers is violated, and we consistently study the large-deviations with respect to the probability of the NN\to\infty limit distribution, yielding a power law, although not exactly a qLDq^{LD}-exponential decay. All qq-statistical parameters which emerge are univocally defined by (η,α)(\eta, \alpha). Finally we discuss the analytical connection with the P\'{o}lya urn problem.

Keywords

Cite

@article{arxiv.1412.0006,
  title  = {Emergence of $q$-statistical functions in a generalized binomial distribution with strong correlations},
  author = {Guiomar Ruiz and Constantino Tsallis},
  journal= {arXiv preprint arXiv:1412.0006},
  year   = {2015}
}

Comments

13 pages, 14 figures