author keywords: Fractional posterior; Generalized linear model; High-dimensional regression; Posterior contraction rate; Sparsity-inducing prior

TL;DR: This work studies posterior contraction rates in sparse high-dimensional generalized linear models using priors incorporating sparsity, and shows that Bayesian methods achieve convergence properties analogous to lasso-type procedures. (via Semantic Scholar)