Model-free Bayesian Clustering and Multimodal Posterior Summarisation
We develop a model-free, noise-aware Bayesian clustering framework that constructs a martingale posterior over clusterings from a predictive Bayesian perspective.
ProofPaper ↗
Key points
- This avoids explicit likelihood-prior model specification, expensive MCMC sampling, and mode-seeking variational approximations, while accommodating irregular cluster shapes and enabling parallel generation of posterior draws.
- To interpret the posterior over high-dimensional, noise-aware clusterings, we introduce a Wasserstein-type summarization method.
- Using a novel noise-aware loss, this approach compresses the large collection of posterior draws into a small set of weighted representatives, capturing distinct cluster structures and noise assignments.
- We establish theoretical properties of the proposed loss and guarantees for the level-marginal posterior approximation.
Sources (1)
- [1]Model-free Bayesian Clustering and Multimodal Posterior SummarisationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 11:10 AM
We develop a model-free, noise-aware Bayesian clustering framework that constructs a martingale posterior over clusterings from a predictive Bayesian perspective.
This avoids explicit likelihood-prior model specification, expensive MCMC sampling, and mode-seeking variational approximations, while accommodating irregular cluster shapes and enabling parallel generation of posterior draws.
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