Scalable Posterior Uncertainty for Flexible Density-Based Clustering
We introduce a novel framework for uncertainty quantification in clustering that combines martingale posterior distributions with density-based clustering. Unlike classical model-based approaches, which define clusters at the latent level of a mixture model, we treat clusters as explicit functionals of the data-generating density, without assuming any specific parametric form. To characterize density uncertainty, we obtain martingale posterior samples via a predictive resampling scheme driven by model score evaluations. This allows us to leverage state-of-the-art differentiable density estimators, such as normalizing flows, making density resampling efficient in large-scale settings and f...
| Authors | Nicola Bariletto, Stephen G. Walker |
|---|---|
| Primary category | stat.ML |
| Published | 3 March 2026 |
| Last updated at source | 17 April 2026 |
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