Search arXivSearch

arXiv · 0704.3156

How to clean a dirty floor: Probabilistic potential theory and the Dobrushin uniqueness theorem

Abstract

Motivated by the Dobrushin uniqueness theorem in statistical mechanics, we consider the following situation: Let αbe a nonnegative matrix over a finite or countably infinite index set X, and define the "cleaning operators" β_h = I_{1-h} + I_h αfor h: X \to [0,1] (here I_f denotes the diagonal matrix with entries f). We ask: For which "cleaning sequences" h_1, h_2, ... do we have c β_{h_1} ... β_{h_n} \to 0 for a suitable class of "dirt vectors" c? We show, under a modest condition on α, that this occurs whenever \sum_i h_i = \infty everywhere on X. More generally, we analyze the cleaning of subsets Λ\subseteq X and the final distribution of dirt on the complement of Λ. We show that when supp(h_i) \subseteq Λwith \sum_i h_i = \infty everywhere on Λ, the operators β_{h_1} ... β_{h_n} converge as n \to \infty to the "balayage operator" Π_Λ= \sum_{k=0}^\infty (I_Λα)^k I_{Λ^c). These results are obtained in two ways: by a fairly simple matrix formalism, and by a more powerful tree formalism that corresponds to working with formal power series in which the matrix elements of αare treated as noncommuting indeterminates.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thierry de la Rue, Roberto Fernandez, Alan D. Sokal. 2007-04-24. How to clean a dirty floor: Probabilistic potential theory and the Dobrushin uniqueness theorem. https://arxiv.org/abs/0704.3156

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

KEEP EXPLORING

Related papers

The extremal process of a cascading family of branching Brownian motion

We study the asymptotic behaviour of the extremal process of a cascading family of branching Brownian motions. This is a particle system on the real line such that each particle has a type in addition to his position. Particles of type $1$ move on the real line according to Brownian motions and branch at rate $1$ into two children of type $1$. Furthermore, at rate $α$, they give birth to children too of type $2$. Particles of type $2$ move according to standard Brownian motion and branch at rate $1$, but cannot give birth to descendants of type $1$. We obtain the asymptotic behaviour of the extremal process of particles of type $2$.

math.PR

On the Wasserstein distance between a hyperuniform point process and its mean

We study the existence of bounds on the expected $p$-Wasserstein distance between a random measure and its mean under the assumption that the $p$-th centered moments of the counting statistics are controlled uniformly in space. The average Wasserstein transport cost is shown to be bounded from above and from below by some multiples of the number of points. $D$-dimensional versions of those results are also obtained. As a corollary, we prove that for any value of $p\geq 1$ the Ginibre point process can be seen as a perturbed lattice with identically distributed perturbations with a finite $p$-th moment.

math.PR

Breuer-Major Theorems for Hilbert Space-Valued Random Variables

Let $\{X_k\}_{k\in\mathbb Z}$ be a stationary Gaussian process with values in a separable Hilbert space $\mathcal H_1$, and let $G:\mathcal H_1\to\mathcal H_2$ be a measurable map into another separable Hilbert space $\mathcal H_2$. We derive a central limit theorem for the centered normalized partial sums of the Hilbert space-valued subordinated process $\{G[X_k]\}_{k\in\mathbb Z}$. Our result holds under either of two sets of sufficient conditions, formulated in terms of the transformation $G$ and the temporal and cross-sectional dependence structure of $\{X_k\}_{k\in\mathbb Z}$. These conditions coincide in finite dimensions but lead to genuinely different phenomena in the infinite-dimensional setting. The proof relies on the recently developed Fourth Moment Theorem on Hilbert spaces, leveraging tools from the infinite-dimensional Malliavin-Stein framework. We also provide continuous-time and quantitative versions of the central limit theorem. In a series of examples, we recover and strengthen limit theorems for a wide array of statistics relevant in functional data analysis, and present, as an application of our result, a novel limit theorem in the framework of neural operators.

math.PR