Bayesian Lasso Regularization. Lasso regression is a linear regression technique that combines regularization and variable selection. In supervised learning regularization is usually accomplished via L2 Ridge⁸ L1 Lasso⁷ or L2L1 ElasticNet⁹ regularization.
Aug 02 2020 On page 227 the authors provide a Bayesian point of view to both ridge and LASSO regression. The lasso Bayesian lasso and extensions can be done using the monomvn package in R. Autumn Quarter 20062007 Regularization.
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The lasso Bayesian lasso and extensions can be done using the monomvn package in R. Currently it includes a set of computationally efficient MCMC algorithms Gibbs and slice samplers for solving the Bayesian reciprocal LASSO. For now we will focus on analytical regularization techniques since their Bayesian interpretation is more well-defined. For neural networks there are also techniques such as Drop-out.
