  Bayesian regularization is a central tool in modern-day statistical and
machine learning methods. Many applications involve high-dimensional sparse
signal recovery problems. The goal of our paper is to provide a review of the
literature on penalty-based regularization approaches, from Tikhonov (Ridge,
Lasso) to horseshoe regularization.
