The paper ‘Bayesian Semi-structured Subspace Inference’ presents a new method to address the problem of epistemic uncertainty in semi-structured regression models. The researchers propose a Bayesian approximation using subspace inference for these models. They extend subspace inference for joint posterior sampling from a full parameter space for structured effects and a subspace for unstructured effects. The method allows for tunable complexity of the subspace and can capture multiple minima in the loss landscape. Their numerical experiments validate the approach’s efficacy in recovering structured effect parameter posteriors in semi-structured models and approaching the full-space posterior distribution of MCMC for increasing subspace dimension. The method also exhibits competitive predictive performance across simulated and real-world datasets.
Publication date: 24 Jan 2024
Project Page: N/A
Paper: https://arxiv.org/pdf/2401.12950
