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Martingale posteriors for generative classifiers

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  • Bissiri, Pier Giovanni
  • Borrotti, Matteo

Abstract

Generative models for classification are a well-established method in statistics and machine learning. Martingales posteriors provide a computationally feasible method for performing prior-free Bayesian analysis. This paper aims to address the problem of uncertainty quantification through martingale posteriors for generative models for classification. To this aim, a conditionally identically distributed sequence of observations is considered. An empirical analysis is given.

Suggested Citation

  • Bissiri, Pier Giovanni & Borrotti, Matteo, 2026. "Martingale posteriors for generative classifiers," Statistics & Probability Letters, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:stapro:v:231:y:2026:i:c:s016771522500272x
    DOI: 10.1016/j.spl.2025.110627
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