Bayesian Regularization: From Tikhonov to Horseshoe
arXiv:1902.06269
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.
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