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Activity Number: 191 - Misspecification and Robustness: Novel Methods and Innovative Insights
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #320978
Title: Fast Approximate BayesBag Model Selection via Taylor Expansions
Author(s): Neil Archibald Spencer* and Jeffrey Miller
Companies: Harvard University and Harvard TH Chan School of Public Health
Keywords: BayesBag; Robust Bayesian Inference; Model Selection; Computation; Marginal Likelihood Computation; Misspecified Models

In recent years, BayesBag has emerged as an effective remedy for the brittleness of Bayesian model selection under model misspecification. However, computing BayesBag can be prohibitively expensive for large datasets. In this talk, I propose a fast approximation of BayesBag model selection---based on Taylor approximations of the log marginal likelihood---that can achieve results comparable to BayesBag in a fraction of the computation time. I provide concrete bounds on the approximation error and establish conditions under which it converges to zero asymptotically as the dataset grows.

Authors who are presenting talks have a * after their name.

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