Bayesian wasserstein repulsive gaussian mixture models
Abstract
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.
Fields of Study & Subjects
Bayesian Methods and Mixture Models
Statistical Mechanics and Entropy
Markov Chains and Monte Carlo Methods
Publication details
- Journal / source
- Statistics and Computing
- Volume
- 36
- Issue
- 4
- Publication date
- 12 May 2026
- Language
- EN