Variational Bayes for High-Dimensional Linear Regression With Sparse Priors
Variational Bayes for High-Dimensional Linear Regression With Sparse Priors
We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression. Under compatibility conditions on the design matrix, oracle inequalities are derived for the mean-field VB approximation, implying that it converges to the sparse truth at the optimal rate and …