Searcharxiv⌕ Search

arXiv subjects

C. Vignat

Publications and source records attributed to C. Vignat.

At least 55 records · Page 3Linked to original sources
arXiv2009

Old and New Results About Relativistic Hermite Polynomials

The relativistic Hermite polynomials (RHP) were introduced in 1991 by Aldaya et al. in a generalization of the theory of the quantum harmonic oscillator to the relativistic context. These polynomials were later related to the more classical Gegenbauer (or ultraspherical) polynomials in a study by Nagel. Thus some of their properties can be deduced from the properties of the well-known Gegenbauer polynomials, as underlined by M. Ismail. In this report we give new proofs of already known results but also new results about these polynomials. We use essentially three basic tools: the representation of polynomials as moments, the subordination tool and Nagel's identity.

math-ph↗
arXiv2009

On the density of the sum of two independent Student t-random vectors

In this paper, we find an expression for the density of the sum of two independent $d-$dimensional Student $t-$random vectors $\mathbf{X}$ and $\mathbf{Y}$ with arbitrary degrees of freedom. As a byproduct we also obtain an expression for the density of the sum $\mathbf{N}+\mathbf{X}$, where $\mathbf{N}$ is normal and $\mathbf{X}$ is an independent Student $t-$vector. In both cases the density is given as an infinite series \[ \sum_{n=0}^{\infty} c_{n}f_{n} \] where $f_{n}$ is a sequence of probability densities on $\mathbb{R}^{d}$ and $(c_{n} )$ is a sequence of positive numbers of sum 1, i.e. the distribution of a non-negative integer-valued random variable $C$, which turns out to be infinitely divisible for $d=1$ and $d=2.$ When $d=1$ and the degrees of freedom of the Student variables are equal, we recover an old result of Ruben.

math.PR↗
arXiv2009

Cariñena polynomials are Jacobi polynomials

We show that the Cariñena orthogonal polynomials are Jacobi polynomials; moreover, there exists a natural bijection between the negative and the positive curvature cases. These results hold only in the two dimensional case.

math.CA↗
arXiv2009

Geometry of the Central Limit Theorem in the Nonextensive Case

We uncover geometric aspects that underlie the sum of two independent stochastic variables when both are governed by q-Gaussian probability distributions. The pertinent discussion is given in terms of random vectors uniformly distributed on a p-sphere.

cond-mat.stat-mech↗
arXiv2008

An entropic view of Pickands' theorem

It is shown that distributions arising in Renyi-Tsallis maximum entropy setting are related to the Generalized Pareto Distributions (GPD) that are widely used for modeling the tails of distributions. The relevance of such modelization, as well as the ubiquity of GPD in practical situations follows from Balkema-De Haan-Pickands theorem on the distribution of excesses (over a high threshold). We provide an entropic view of this result, by showing that the distribution of a suitably normalized excess variable converges to the solution of a maximum Tsallis entropy, which is the GPD. This highlights the relevance of the so-called Tsallis distributions in many applications as well as some relevance to the use of the corresponding entropy.

cs.IT↗
arXiv2008

On some results of Cufaro Petroni about Student t-processes

This paper deals with Student t-processes as studied in (Cufaro Petroni N 2007 J. Phys. A, Math. Theor. 40(10), 2227-2250). We prove and extend some conjectures expressed by Cufaro Petroni about the asymptotical behavior of a Student t-process and the expansion of its density. First, the explicit asymptotic behavior of any real positive convolution power of a Student t-density with any real positive degrees of freedom is given in the multivariate case; then the integer convolution power of a Student t-distribution with odd degrees of freedom is shown to be a convex combination of Student t-densities with odd degrees of freedom. At last, we show that this result does not extend to the case of non-integer convolution powers.

math.PR↗
arXiv2007

An extension of Wick's theorem

We propose an extension of a result by Repetowicz et al. about Wick's theorem and its applications: we first show that Wick's theorem can be extended to the uniform distribution on the sphere and then to the whole class of elliptical distributions. Then, as a special case, we detail this result for distributions that are Gaussian scale mixtures. Finally, we show that these results allow to recover easily a theorem by Folland about integration of polynomials over the sphere.

cond-mat.stat-mech↗
arXiv2007

Detecting q-Gaussian distributions and the normalization effect

We show that whenever data are gathered using a device that performs a normalization-preprocessing, the ensuing normalized input, as recorded by the measurement device, will always be q-Gaussian distributed if the incoming data exhibit elliptical symmetry. As a consequence, great care should be exercised when detecting q-Gaussians. As an example, Gaussian data will appear, after normalization, in the guise of q-Gaussian records. Moreover, we show that the value of the resulting parameter q can be deduced from the normalization technique that characterizes the device.

cond-mat.stat-mech↗
arXiv2007

Central limit theorem, deformed exponentials and superstatistics

We show that there exists a very natural, superstatistics-linked extension of the central limit theorem (CLT) to deformed exponentials (also called q-Gaussians): This generalization favorably compares with the one provided by S. Umarov and C. Tsallis [arXiv:cond-mat/0703533], since the latter requires a special "q-independence" condition on the data. On the contrary, our CLT proposal applies exactly in the usual conditions in which the classical CLT is used. Moreover, we show that, asymptotically, the q-independence condition is naturally induced by our version of the CLT.

cond-mat.stat-mech↗
arXiv2006

Scale invariance and related properties of q-Gaussian systems

We advance scale-invariance arguments for systems that are governed (or approximated) by a $q-$Gaussian distribution, i.e., a power law distribution with exponent $Q=1/(1-q); q \in \mathbb{R}$. The ensuing line of reasoning is then compared with that applying for Gaussian distributions, with emphasis on dimensional considerations. In particular, a Gaussian system may be part of a larger system that is not Gaussian, but, if the larger system is spherically invariant, then it is necessarily Gaussian again. We show that this result extends to q-Gaussian systems via elliptic invariance. The problem of estimating the appropriate value for $q$ is revisited. A kinetic application is also provided.

cond-mat.stat-mech↗
arXiv2006

Power-law random walks

We present some new results about the distribution of a random walk whose independent steps follow a $q-$Gaussian distribution with exponent $\frac{1}{1-q}; q \in \mathbb{R}$. In the case $q>1$ we show that a stochastic representation of the point reached after $n$ steps of the walk can be expressed explicitly for all $n$. In the case $q<1,$ we show that the random walk can be interpreted as a projection of an isotropic random walk, i.e. a random walk with fixed length steps and uniformly distributed directions.

cond-mat.stat-mech↗
arXiv2006

Estimation in a fluctuating medium and power-law distributions

We show how recent results by Bening and Korolev in the context of estimation, when linked with a classical result of Fisher concerning the negative binomial distribution, can be used to explain the ubiquity of power law probability distributions. Beck, Cohen and others have provided plausible mechanisms explaining how power law probability distributions naturally emerge in scenarios characterized by either finite dimension or fluctuation effects. This paper tries to further contribute to such an idea. As an application, a new and multivariate version of the central limit theorem is obtained that provides a convenient alternative to the one recently presented in [S. Umarov, C. Tsallis, S. Steinberg, cond-mat/0603593].

cond-mat.stat-mech↗
arXiv2006

On classes of non-Gaussian asymptotic minimizers in entropic uncertainty principles

In this paper we revisit the Bialynicki-Birula & Mycielski uncertainty principle and its cases of equality. This Shannon entropic version of the well-known Heisenberg uncertainty principle can be used when dealing with variables that admit no variance. In this paper, we extend this uncertainty principle to Renyi entropies. We recall that in both Shannon and Renyi cases, and for a given dimension n, the only case of equality occurs for Gaussian random vectors. We show that as n grows, however, the bound is also asymptotically attained in the cases of n-dimensional Student-t and Student-r distributions. A complete analytical study is performed in a special case of a Student-t distribution. We also show numerically that this effect exists for the particular case of a n-dimensional Cauchy variable, whatever the Renyi entropy considered, extending the results of Abe and illustrating the analytical asymptotic study of the student-t case. In the Student-r case, we show numerically that the same behavior occurs for uniformly distributed vectors. These particular cases and other ones investigated in this paper are interesting since they show that this asymptotic behavior cannot be considered as a "Gaussianization" of the vector when the dimension increases.

math.PR↗
arXiv2005

Correlated Gaussian systems exhibiting additive power-law entropies

We show, on purely statistical grounds and without appeal to any physical model, that a power-law $q-$entropy $S_q$, with $0<q<1$, can be {\it extensive}. More specifically, if the components $X_i$ of a vector $X \in \mathbb{R}^N$ are distributed according to a Gaussian probability distribution $f$, the associated entropy $S_q(X)$ exhibits the extensivity property for special types of correlations among the $X_i$. We also characterize this kind of correlation.

cond-mat.stat-mech↗
arXiv2005

Poincaré's Observation and the Origin of Tsallis Generalized Canonical Distributions

In this paper, we present some geometric properties of the maximum entropy (MaxEnt) Tsallis- distributions under energy constraint. In the case q > 1, these distributions are proved to be marginals of uniform distributions on the sphere; in the case q < 1, they can be constructed as conditional distribu- tions of a Cauchy law built from the same uniform distribution on the sphere using a gnomonic projection. As such, these distributions reveal the relevance of using Tsallis distributions in the microcanonical setup: an example of such application is given in the case of the ideal gas.

cond-mat.stat-mech↗
arXiv2005

Superstatistics Based on the Microcanonical Ensemble

Superstatistics is a "statistics" of "canonical-ensemble statistics". In analogy, we consider here a similar theoretical construct, but based upon the microcanonical ensemble. The mixing parameter is not the temperature but the index q associated with the non-extensive, power law entropy Sq.

cond-mat.stat-mech↗
arXiv2005

Density operators that extremize Tsallis entropy and thermal stability effects

Quite general, analytical (both exact and approximate) forms for discrete probability distributions (PD's) that maximize Tsallis entropy for a fixed variance are here investigated. They apply, for instance, in a wide variety of scenarios in which the system is characterized by a series of discrete eigenstates of the Hamiltonian. Using these discrete PD's as "weights" leads to density operators of a rather general character. The present study allows one to vividly exhibit the effects of non-extensivity. Varying Tsallis' non-extensivity index $q$ one is seen to pass from unstable to stable systems and even to unphysical situations of infinite energy.

cond-mat.stat-mech↗