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Bayesian inference paper questions predictive model calibration and concentration

A new paper published on arXiv explores the reliability of Predictive Bayesian Inference (PBI). The research demonstrates that PBI's posterior concentration is dependent on the forward predictive model used, which also entirely dictates the uncertainty quantification. The study highlights that if the predictive model fails to capture all relevant data features, the coverage of PBI credible sets can be significantly reduced, potentially approaching zero. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Highlights potential calibration issues in Bayesian inference methods, impacting the reliability of uncertainty quantification in statistical models.

RANK_REASON Academic paper on statistical methodology.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · David T. Frazier, Hui Wang ·

    Concentration and Calibration in Predictive Bayesian Inference

    arXiv:2605.00455v1 Announce Type: cross Abstract: Predictive Bayesian inference (PBI) represents a model-and prior-agnostic approach to standard Bayesian inference which allows users to quantify uncertainty for a functional of interest only by specifying a forward predictive mode…

  2. arXiv stat.ML TIER_1 · Hui Wang ·

    Concentration and Calibration in Predictive Bayesian Inference

    Predictive Bayesian inference (PBI) represents a model-and prior-agnostic approach to standard Bayesian inference which allows users to quantify uncertainty for a functional of interest only by specifying a forward predictive model for future unobserved data. The flexibility and …