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Bayesian regularization: From Tikhonov to horseshoe

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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. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust Methods Statistical Models > Linear Models Statistical Models > Bayesian Models
Comparison of geometry of a unit ball induced by Normal, Laplace, Cauchy and Horseshoe priors
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Posterior mode for each of each of the 10 betas estimated using Laplace, normal and horseshoe Bayesian models as well as OLS estimates
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Comparison of Laplace (LASSO), Normal (Ridge), Cauchy and Horseshoe priors
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Condition number of original problem (left) and the regularized one (right)
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Statistical Models > Bayesian Models
Statistical Models > Linear Models
Statistical and Graphical Methods of Data Analysis > Robust Methods

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