# An Expectation Maximization Algorithm for High-Dimensional Model Selection for the Ising Model with Misclassified States

## TL;DR

This paper introduces an EM algorithm for high-dimensional Ising model selection that accounts for misclassified binary states, improving accuracy in dependent binary data analysis.

## Contribution

It extends existing model selection methods to handle misclassification, providing a new EM-based approach for more accurate graphical model identification.

## Key findings

- The EM algorithm improves model selection accuracy with simulated data.
- Application to fMRI data demonstrates practical effectiveness.
- Theoretical guarantees for edge identification under misclassification.

## Abstract

We propose the misclassified Ising Model; a framework for analyzing dependent binary data where the binary state is susceptible to error. We extend the theoretical results of the model selection method presented in Ravikumar et. al. (2010) to show that the method will still correctly identify edges in the underlying graphical model under suitable misclassification settings. With knowledge of the misclassification process, an expectation maximization algorithm is developed that accounts for misclassification during model selection. We illustrate the increase of performance of the proposed expectation maximization algorithm with simulated data, and using data from a functional magnetic resonance imaging analysis.