AI RESEARCH
On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
arXiv CS.LG
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ArXi:2603.22030v1 Announce Type: new Bayesian neural network (BNN) posteriors are often considered impractical for inference, as symmetries fragment them, non-identifiabilities inflate dimensionality, and weight-space priors are seen as meaningless. In this work, we study how overparametrization and priors together reshape BNN posteriors and derive implications allowing us to better understand their interplay. We show that redundancy