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Invariant manifolds provide the geometric structures for describing and understanding dynamics of nonlinear systems. The theory of invariant manifolds for both finite and infinite dimensional autonomous deterministic systems, and for…

Dynamical Systems · Mathematics 2007-05-23 Jinqiao Duan , Kening Lu , Bjoern Schmalfuss

We address the construction of stable random matrix ensembles as the generalization of the stable random variables (Levy distributions). With a simple method we derive the Cauchy case, which is known to have remarkable properties. These…

Statistical Mechanics · Physics 2007-05-23 M. Tierz

Large random matrices appear in different fields of mathematics and physics such as combinatorics, probability theory, statistics, operator theory, number theory, quantum field theory, string theory etc... In the last ten years, they…

Probability · Mathematics 2007-05-23 Alice Guionnet

Motivated by limits of critical inhomogeneous random graphs, we construct a family of sequences of measured metric spaces that we call continuous multiplicative graphs, that are expected to be the universal limit of graphs related to the…

Probability · Mathematics 2020-02-07 Nicolas Broutin , Thomas Duquesne , Minmin Wang

A stochastic model, the product of a circulant matrix and a random normal vector, is shown to produce an evolutive long memory time series with a power law spectral density. The distribution of the time series, a beta location scale family…

General Mathematics · Mathematics 2026-02-26 Robert Kimberk

The density matrix for the impenetrable Bose gas in Dirichlet and Neumann boundary conditions can be written in terms of $<\prod_{l=1}^n| \cos\phi_1-\cos\theta_l| |\cos\phi_2-\cos\theta_l|>$, where the average is with respect to the…

Mathematical Physics · Physics 2015-06-26 P. J. Forrester , N. E. Frankel , T. M. Garoni

Using the standard concepts of free random variables, we show that for a large class of nonhermitean random matrix models, the support of the eigenvalue distribution follows from their hermitean analogs using a conformal transformation. We…

High Energy Physics - Phenomenology · Physics 2009-10-28 Romuald A. Janik , Maciej A. Nowak , Gabor Papp , Jochen Wambach , Ismail Zahed

A quasi-infinitely divisible distribution on $\mathbb{R}^d$ is a probability distribution $\mu$ on $\mathbb{R}^d$ whose characteristic function can be written as the quotient of the characteristic functions of two infinitely divisible…

Probability · Mathematics 2021-01-08 David Berger , Merve Kutlu , Alexander Lindner

This paper first surveys the connection of integrable systems of the Painleve type to various distribution functions appearing in Wigner-Dyson random matrix theory. A short discussion is then given of the appearance of these same…

solv-int · Physics 2007-05-23 Craig A. Tracy , Harold Widom

This paper provides a framework for investigations in fluctuation theory for L\'evy processes with matrix-exponential jumps. We present a matrix form of the components of the infinitely divisible factorization. Using this representation we…

Probability · Mathematics 2014-12-09 Ievgen Karnaukh

Statistical system models provide the basis for the examination of various sorts of distributions. Classification distributions are a very common and versatile form of statistics in e.g. real economic, social, and IT systems. The…

Computation · Statistics 2019-12-20 Uwe Petersohn , Thomas Dedek , Sandra Zimmer , Hans Biskupski

This paper gives a complete characterization of infinitely divisible semimartingales, i.e., semimartingales whose finite dimensional distributions are infinitely divisible. An explicit and essentially unique decomposition of such…

Probability · Mathematics 2014-05-02 Andreas Basse-O'Connor , Jan Rosinski

To each hyperbolic Landau level of the Poincar\'e disc is attached a generalized negative binomial distribution. In this paper, we compute the moment generating function of this distribution and supply its decomposition as a perturbation of…

Probability · Mathematics 2016-08-03 Hassan Chhaiba , Nizar Demni , Zouhair Mouayn

We prove that independent families of permutation invariant random matrices are asymptotically free over the diagonal, both in probability and in expectation, under a uniform boundedness assumption on the operator norm. We can relax the…

Probability · Mathematics 2022-04-26 Benson Au , Guillaume Cébron , Antoine Dahlqvist , Franck Gabriel , Camille Male

We construct explicit invariant measures for a family of infinite products of random, independent, identically-distributed elements of SL(2,C). The matrices in the product are such that one entry is gamma-distributed along a ray in the…

Mathematical Physics · Physics 2007-05-23 Jens Marklof , Yves Tourigny , Lech Wolowski

We derive expressions for the probability distribution of the ratio of two consecutive level spacings for the classical ensembles of random matrices. This ratio distribution was recently introduced to study spectral properties of many-body…

Mathematical Physics · Physics 2013-02-27 Y. Y. Atas , E. Bogomolny , O. Giraud , G. Roux

A $d$-dimensional random array on a nonempty set $I$ is a stochastic process $\boldsymbol{X}=\langle X_s:s\in \binom{I}{d}\rangle$ indexed by the set $\binom{I}{d}$ of all $d$-element subsets of $I$. We obtain structural decompositions of…

Probability · Mathematics 2025-02-18 Pandelis Dodos , Konstantinos Tyros , Petros Valettas

An individual-based model of an infinite system of point particles in $\mathbb{R}^d$ is proposed and studied. In this model, each particle at random produces a finite number of new particles and disappears afterwards. The phase space for…

Dynamical Systems · Mathematics 2015-10-27 Agnieszka Tanaś

We discuss isoperimetric inequalities for convex sets. These include the classical isoperimetric inequality and that of Brunn-Minkowski, Blaschke-Santalo, Busemann-Petty and their various extensions. We show that many such inequalities…

Metric Geometry · Mathematics 2016-07-05 Grigoris Paouris , Peter Pivovarov

This paper formalizes a latent variable inference problem we call {\em supervised pattern discovery}, the goal of which is to find sets of observations that belong to a single ``pattern.'' We discuss two versions of the problem and prove…

Machine Learning · Statistics 2014-02-10 Jonathan H. Huggins , Cynthia Rudin
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