Scalable Posterior Uncertainty for Flexible Density-Based Clustering
The authors developed a framework combining martingale posterior distributions with density-based clustering to quantify uncertainty in clustering. This method treats clusters as functions of the data's underlying density, not latent mixture parameters. It allows for flexible cluster shapes and scalable uncertainty estimation using score-based updates and differentiable density estimators like normalizing flows.