Fast Approximate BayesBag Model Selection via Taylor Expansions

Presented by Neil Spencer, Postdoctoral Researcher, Department of Biostatistics, Harvard School of Public Health

Tuesday, January 18, 2022
4:00 p.m. ET
Online

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, which can achieve results comparable to BayesBag in a fraction of the computation time. I provide concrete bounds on the approximation error and establish that it converges to zero asymptotically as the dataset grows. I demonstrate the utility of this approach using simulations, as well as model selection Problems arising in business, neuroscience, and forensics.

Speaker Bio

Dr. Spencer is currently a postdoctoral researcher in the Department of Biostatistics at Harvard School of Public Health. He works with Jeff Miller developing robust Bayesian methodology for biomedical applications, including biostatistical analysis of X-linked Dystonia Parkinsonism. He holds a joint PhD in Statistics and Machine Learning from Carnegie Mellon University, a MSc in Statistics from the University of British Columbia, and a BScH in Mathematics and Statistics from Acadia University. His methodological interests include robust Bayesian inference, statistical network modeling, and Monte Carlo methods. In terms of applications, he has worked in forensic science, medicine, neuroscience, and education.