# Equivalence Set Restricted Latent Class Models (ESRLCM)

arXiv: https://arxiv.org/abs/2406.03653 · HTML: https://mlsanity.com/paper/2406.03653/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Identifiability of restricted latent class models with binary responses](https://mlsanity.com/paper/1603.04140/) (2016)
- [Tree-Regularized Bayesian Latent Class Analysis for Improving Weakly Separated Dietary Pattern Subtyping in Small-Sized Subpopulations](https://mlsanity.com/paper/2306.04700/) (2023)

## Abstract (arXiv)

Latent Class Models (LCMs) are used to cluster multivariate categorical data, commonly used to interpret survey responses. We propose a novel Bayesian model called the Equivalence Set Restricted Latent Class Model (ESRLCM). This model identifies clusters who have common item response probabilities, and does so more generically than traditional restricted latent attribute models. We verify the identifiability of ESRLCMs, and demonstrate the effectiveness in both simulations and real-world applications.