Bayesian Regularization: From Tikhonov to Horseshoe

Abstract

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.

References in corpus (1)

• Sparsity information and regularization in the horseshoe and other shrinkage priors