Bayesian Regularization: From Tikhonov to Horseshoe

02/17/2019

∙

by   Nicholas G. Polson, et al.

∙

0

∙

share

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.