Poster
in
Workshop: Your Model is Wrong: Robustness and misspecification in probabilistic modeling

Fast approximate BayesBag model selection via Taylor expansions

Neil Spencer ⋅ Jeffrey Miller

Abstract

BayesBag has been established as a useful tool for robust Bayesian model selection. However, computing BayesBag can be prohibitively expensive for large datasets. Here, we propose a fast approximation of BayesBag model selection. This approximation---based on Taylor approximations of the log marginal likelihood---can achieve results comparable to BayesBag in a fraction of the time.

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