Learning Mixtures of Ising Models using Pseudolikelihood

TL;DR

This paper develops a pseudolikelihood-based method for learning parameters of mixtures of Ising models, demonstrating its effectiveness on synthetic and real datasets for Ising and Potts models.

Contribution

It introduces a novel pseudolikelihood approach tailored for mixture Ising models, expanding the applicability of pseudolikelihood methods.

Findings

Effective parameter learning on synthetic data

Successful application to real-world datasets

Applicable to both Ising and Potts models

Abstract

Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models. In this paper, we study how pseudolikelihood can be derived for learning parameters of a mixture of Ising models. The performance of the proposed approach is demonstrated for Ising and Potts models on both synthetic and real data.