{
  "abstract": "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. 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. Further, we marginalise the uncertainty in the density level, avoiding reliance on a specific choice. We establish theoretical properties of the proposed loss and guarantees for the level-marginal posterior approximation. Results demonstrate the framework's effectiveness in capturing clustering and noise uncertainty.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.13397",
  "author_total": 2,
  "authors": [
    {
      "name": "Qing Li"
    },
    {
      "name": "Sara Wade"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9587dp7nye9rm.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.13397",
  "fulltext_url": null,
  "id": "9587dp7nye9rm",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.13397",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Bayesian Methods and Mixture Models"
    ]
  ],
  "openalex": "W7221535008",
  "pdf_url": "https://arxiv.org/pdf/2610.13397",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9587dp7nye9rm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:46Z",
      "native_id": "2610.13397",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.13397"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Model-free Bayesian Clustering and Multimodal Posterior Summarisation",
  "title_text": "Model-free Bayesian Clustering and Multimodal Posterior Summarisation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:46Z",
  "url": "https://research.draconitic.com/paper/9587dp7nye9rm",
  "venue": "arXiv",
  "versions": []
}