Bayesian Regularization: From Tikhonov to Horseshoe
Methodology
2019-02-19 v1
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.
Cite
@article{arxiv.1902.06269,
title = {Bayesian Regularization: From Tikhonov to Horseshoe},
author = {Nicholas G. Polson and Vadim Sokolov},
journal= {arXiv preprint arXiv:1902.06269},
year = {2019}
}