Inference in Ising Models

TL;DR

This paper analyzes the consistency and limitations of the maximum pseudolikelihood estimator in Ising models, providing new convergence rates and demonstrating testing impossibility in high-temperature regimes.

Contribution

It extends existing results by establishing general consistency rates for the MPLE in ferromagnetic Ising models on weighted graphs and characterizes testing limitations in high-temperature phases.

Findings

MPLE is $ ext{sqrt}(a_N)$-consistent near certain points.

Testing is impossible in high-temperature regimes.

Asymptotic power of tests is characterized.

Abstract

The Ising spin glass is a one-parameter exponential family model for binary data with quadratic sufficient statistic. In this paper, we show that given a single realization from this model, the maximum pseudolikelihood estimate (MPLE) of the natural parameter is $\sqrt {a_N}$-consistent at a point whenever the log-partition function has order $a_N$ in a neighborhood of that point. This gives consistency rates of the MPLE for ferromagnetic Ising models on general weighted graphs in all regimes, extending the results of Chatterjee (2007) where only $\sqrt N$-consistency of the MPLE was shown. It is also shown that consistent testing, and hence estimation, is impossible in the high temperature phase in ferromagnetic Ising models on a converging sequence of simple graphs, which include the Curie--Weiss model. In this regime, the sufficient statistic is distributed as a weighted sum of independent $\chi^2_1$ random variables, and the asymptotic power of the most powerful test is determined. We also illustrate applications of our results on synthetic and real-world network data.