Critical probabilities for positively associated, finite-range dependent percolation models

TL;DR

This paper proves that positively associated, finite-range dependent percolation models on trees and graphs with bounded degree percolate above critical probabilities, and introduces a parameter for percolation in 1-independent models, providing bounds and generalizations.

Contribution

It establishes that positive association ensures percolation above critical probability on trees and extends stochastic domination results to arbitrary marginals on general graphs, also introducing a new parameter for 1-independent models.

Findings

Positively associated, finite-range dependent models percolate above critical probability on trees.

Stochastic domination holds for all marginals under positive association on bounded degree graphs.

Bounds on percolation thresholds for 1-independent models on  and ^n, including oriented percolation.

Abstract

On a locally finite, infinite tree $T$, let $p_c(T)$ denote the critical probability for Bernoulli percolation. We prove that every positively associated, finite-range dependent percolation model on $T$ with marginals $p > p_c(T)$ must percolate. Among finite-range dependent models on trees, positive association is thus a favourable property for percolation to occur. On general graphs of bounded degree, Liggett, Schonmann and Stacey (1997) proved that finite-range dependent percolation models with sufficiently large marginals stochastically dominate product measures. Under the additional assumption of positive association, we prove that stochastic domination actually holds for arbitrary marginals. Our result thereby generalises Proposition 3.4 in Liggett, Schonmann and Stacey (1997) which was restricted to the special case $G = \mathbb{Z}$. Studying the class of 1-independent percolation models has proven useful in bounding critical probabilities of various percolation models via renormalization. In many cases, the renormalized model is not only 1-independent but also positively associated. This motivates us to introduce the smallest parameter $p_a^+(G)$ such that every positively associated, 1-independent bond percolation model on a graph $G$ with marginals $p > p_a^+(G)$ percolates. We obtain quantitative upper and lower bounds on $p_a^+(\mathbb{Z}^2)$ and on $p_a^+(\mathbb{Z}^n)$ as $n\to \infty$, and also study the case of oriented bond percolation. In proving these results, we revisit several techniques originally developed for Bernoulli percolation, which become applicable thanks to a simple but seemingly new way of combining positive association with finite-range dependence.