SearcharxivSearch

arXiv · math/0205086

Random and universal metric spaces

Abstract

We introduce a model of the set of all Polish (=separable complete metric) spaces: the cone $\cal R$ of distance matrices, and consider geometric and probabilistic problems connected with this object. The notion of the universal distance matrix is defined and we proved that the set of such matrices is everywhere dense $G_δ$ set in weak topology in the cone $\cal R$. Universality of distance matrix is the necessary and sufficient condition on the distance matrix of the countable everywhere dense set of so called universal Urysohn space which he had defined in 1924 in his last paper. This means that Urysohn space is generic in the set of all Polish spaces. Then we consider metric spaces with measures (metric triples) and define a complete invariant: its - matrix distribution. We give an intrinsic characterization of the set of matrix distributions, and using the ergodic theorem, give a new proof of Gromov's ``reconstruction theorem'. A natural construction of a wide class of measures on the cone $\cal R$ is given and for these we show that {\it with probability one a random Polish space is again the Urysohn space}. There is a close connection between these questions, metric classification of measurable functions of several arguments, and classification of the actions of the infinite symmetric group [V1,V2].

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

A. Vershik. 2002-09-02. Random and universal metric spaces. https://arxiv.org/abs/math/0205086

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection

In this paper, we study averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection. First, we derive a general averaging principle applicable to such equations under minimal assumptions. Subsequently, since the coefficients of the obtained averaged equation still depend on the small scaling parameter $\e$, we impose either periodic or asymptotic conditions on the coefficients, thereby obtain two distinct averaged equations whose coefficients are independent of $\e$ and establish two averaging principles. Stopping times and Khasminskii's time discretization schemes play an important role. Finally, a concrete example is provided to illustrate the applicability and validity of the theoretical results.

math.PR

Spectral properties of Random Matrices

We give the theoretical foundations of random matrix theory through the definitions of a random matrix, a random probability measure and the corresponding empirical spectral distribution. The technical tool we use is the Stieltjes transform method through which we prove optimal convergence of the empirical spectral distribution of random sample covariance matrices to the deterministic Marchenko-Pastur distribution. We also give new results about the rigidity of the eigenvalues of this random sample covariance matrix and the rate of their convergence. We then define the Dyson equation method to prove new local laws about a random matrix model that interpolates between the Marchenko-Pastur distribution, the elliptical law and the circular law. Through our work these local laws can be considered universal.

math.PR

Moments approach for the elephant random walk

We discuss the method of moments for the one-dimensional elephant random walk (ERW). We first derive a differential recurrence relation for the characteristic function of the ERW, which yields a corresponding system of recurrence relations for its moments. We then obtain asymptotic approximations for the moments in each of the three parameter regimes of the ERW. Finally, by establishing the convergence of the moments and verifying the corresponding moment-determinacy conditions, we identify the limiting distributions of the ERW in each regime.

math.PR