Priors on the Variance in Sparse Bayesian Learning ; the demi-Bayesian Lasso

    Abstract

    We explore the use of proper priors for variance parameters of certain sparse Bayesian regression models. This leads to a connection between sparse Bayesian learning (SBL) models (Tipping, 2001) and the recently proposed Bayesian Lasso (Park and Casella, 2008). We outline simple modifications of existing algorithms to solve this new variant which… (More)

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