Group Spike-and-Slab Variational Bayes
File(s)
Published version
Author(s)
Komodromos, Michael
Evangelou, Marina
Filippi, Sarah
Ray, Kolyan
Type
Journal Article
Abstract
We introduce Group Spike-and-Slab Variational Bayes (GSVB), a scalable method for group sparse regression. A fast co-ordinate ascent variational inference (CAVI) algorithm is developed for several common model families including Gaussian, Binomial and Poisson. Theoretical guarantees for our proposed approach are provided by deriving contraction rates for the variational posterior in grouped linear regression. Through extensive numerical studies, we demonstrate that GSVB provides state-of-the-art performance, offering a computationally inexpensive substitute to Markov Chain Monte Carlo (MCMC), whilst performing comparably or better than existing maximum a posteriori (MAP) methods. Additionally, we analyze three real world datasets wherein we highlight the practical utility of our method, demonstrating that GSVB provides parsimonious models with excellent predictive performance, variable selection and uncertainty quantification.
Date Issued
2025-09-04
Date Acceptance
2025-06-21
Citation
Bayesian Analysis, 2025
ISSN
1936-0975
Publisher
International Society for Bayesian Analysis
Journal / Book Title
Bayesian Analysis
Copyright Statement
Copyright © 2025 International Society for Bayesian Analysis. This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published online
Date Publish Online
2025-09-04