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Predictive performance of power posteriors

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arxiv 2408.08806 v2 pith:G3SI3XKR submitted 2024-08-16 math.ST stat.TH

Yann McLatchie , Edwin Fong , David T. Frazier , Jeremias Knoblauch This is my paper
classification math.STstat.TH
keywords impactperformanceposteriorpredictionspredictivesamplesanalysechoice
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We analyse the impact of using tempered likelihoods in the production of posterior predictions. While the choice of temperature has an impact on predictive performance in small samples, we formally show that in moderate-to-large samples, tempering does not impact posterior predictions.

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Cited by 1 Pith paper

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  1. Priors Matter: Addressing Misspecification in Bayesian Deep Q-Learning

    cs.LG 2025-08 conditional novelty 5.0 of 10

    Bayesian deep Q-learning exhibits a cold posterior effect, caused partly by misspecified Gaussian priors, and Laplace or meta-learned priors improve performance.

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