Work

Bayesian wasserstein repulsive gaussian mixture models

Year: 2026

Type: article

Abstract: We develop the Bayesian Wasserstein repulsive Gaussian mixture model that promotes well-separated clusters. Unlike existing repulsive mixture approaches that focus on separating the component means, our method encourages separation between mixture components based on the Wasserstein distance. We establish posterior contraction rates within the framework of nonparametric density estimation. Posterior sampling is performed using a blocked-collapsed Gibbs sampler. Through simulation studies and real data applications, we demonstrate the effectiveness of the proposed model.

Source: Statistics and Computing

DOI: 10.1007/s11222-026-10887-9

Authors: Weipeng Huang, Tin Lok James Ng

Institution: Trinity College Dublin

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,890

Funders: Shenzhen Institute of Information Technology, Trinity College Dublin