# Mixtures of Experts Models

arXiv: https://arxiv.org/abs/1806.08200 · HTML: https://mlsanity.com/paper/1806.08200/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives](https://mlsanity.com/paper/2107.11317/) (2021)
- [Training of Neural Networks with Uncertain Data: A Mixture of Experts Approach](https://mlsanity.com/paper/2312.08083/) (2023)
- [Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score](https://mlsanity.com/paper/2111.02302/) (2021)
- [A parallelizable model-based approach for marginal and multivariate clustering](https://mlsanity.com/paper/2212.04009/) (2022)
- [Liu-type Shrinkage Estimators for Mixture of Poisson Regressions with Experts: A Heart Disease Study](https://mlsanity.com/paper/2309.05838/) (2023)
- [OTSS: Output-Targeted Soft Segmentation for Contextual Decision-Weight Learning](https://mlsanity.com/paper/2605.00193/) (2026)

## Abstract (arXiv)

Mixtures of experts models provide a framework in which covariates may be included in mixture models. This is achieved by modelling the parameters of the mixture model as functions of the concomitant covariates. Given their mixture model foundation, mixtures of experts models possess a diverse range of analytic uses, from clustering observations to capturing parameter heterogeneity in cross-sectional data. This chapter focuses on delineating the mixture of experts modelling framework and demonstrates the utility and flexibility of mixtures of experts models as an analytic tool.