Analysis shows that Bayesian Lasso performs similarly to Lasso under specific noise conditions, suggesting efficient modeling techniques.
We show that Lasso and Bayesian Lasso are very close when the sparsity is large and the noise is small. We propose to solve Bayesian Lasso using multivalued stochastic differential equation. We derive four discretization algorithms, and present highly efficient multilevel Monte Carlo (MLMC) simulations. Additionally, we perform a numerical comparison of the Monte Carlo (MC), MLMC and proximal Markov chain Monte Carlo algorithm (PMALA).
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Rahmania et al. (2025) studied this question.
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