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

Madigan, David

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 essentially uses type-II maximum likelihood to fit the Bayesian Lasso model. We also propose an Elastic-net (Zou and Hastie, 2005) heuristic to help with modeling correlated inputs. Experimental results show the proposals to compare favorably to both the Lasso and traditional and more recent sparse Bayesian algorithms.

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August 19, 2009
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