# Dependent Latent Class Models

arXiv: https://arxiv.org/abs/2205.08677 · HTML: https://mlsanity.com/paper/2205.08677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 4 of 4)

- [Understanding predictive information criteria for Bayesian models](https://mlsanity.com/paper/1307.5928/) (2013)
- [Bayesian Pyramids: Identifiable Multilayer Discrete Latent Structure Models for Discrete Data](https://mlsanity.com/paper/2101.10373/) (2021)
- [Blessing of Dependence: Identifiability and Geometry of Discrete Models with Multiple Binary Latent Variables](https://mlsanity.com/paper/2203.04403/) (2022)
- [Finite mixture model of conditional dependencies modes to cluster categorical data](https://mlsanity.com/paper/1402.5103/) (2014)

## Abstract (arXiv)

Latent Class Models (LCMs) are used to cluster multivariate categorical data (e.g. group participants based on survey responses). Traditional LCMs assume a property called conditional independence. This assumption can be restrictive, leading to model misspecification and overparameterization. To combat this problem, we developed a novel Bayesian model called a Dependent Latent Class Model (DLCM), which permits conditional dependence. We verify identifiability of DLCMs. We also demonstrate the effectiveness of DLCMs in both simulations and real-world applications. Compared to traditional LCMs, DLCMs are effective in applications with time series, overlapping items, and structural zeroes.