Inferring the dependence graph density of binary graphical models in high dimension

TL;DR

This paper develops a method to estimate the connectivity probability in high-dimensional binary graphical models using observed data, with proven convergence rates and a novel sampling technique.

Contribution

It introduces an estimator for the Erd"os-Rényi graph parameter p based on observed chains, with analysis of correlation decay and a new sampling method.

Findings

Estimator for p converges at rate N^{-1/2}+N^{1/2}/T+( ext{log}(T)/T)^{1/2}.

Backward regeneration representation enables perfect sampling from the stationary distribution.

Analysis of correlation decay informs the estimation procedure.

Abstract

We consider a system of binary interacting chains describing the dynamics of a group of $N$ components that, at each time unit, either send some signal to the others or remain silent otherwise. The interactions among the chains are encoded by a directed Erd\"os-R\'enyi random graph with unknown parameter $ p \in (0, 1) .$ Moreover, the system is structured within two populations (excitatory chains versus inhibitory ones) which are coupled via a mean field interaction on the underlying Erd\"os-R\'enyi graph. In this paper, we address the question of inferring the connectivity parameter $p$ based only on the observation of the interacting chains over $T$ time units. In our main result, we show that the connectivity parameter $p$ can be estimated with rate $N^{-1/2}+N^{1/2}/T+(\log(T)/T)^{1/2}$ through an easy-to-compute estimator. Our analysis relies on a precise study of the spatio-temporal decay of correlations of the interacting chains. This is done through the study of coalescing random walks defining a backward regeneration representation of the system. Interestingly, we also show that this backward regeneration representation allows us to perfectly sample the system of interacting chains (conditionally on each realization of the underlying Erd\"os-R\'enyi graph) from its stationary distribution. These probabilistic results have an interest in its own.