Bayesian Wasserstein Repulsive Gaussian Mixture Models
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915267283517440 |
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| author | Huang, Weipeng Ng, Tin Lok James |
| author_facet | Huang, Weipeng Ng, Tin Lok James |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21391 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Wasserstein Repulsive Gaussian Mixture Models Huang, Weipeng Ng, Tin Lok James Methodology 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. |
| title | Bayesian Wasserstein Repulsive Gaussian Mixture Models |
| topic | Methodology |
| url | https://arxiv.org/abs/2504.21391 |