Comparer des méthodes

	Régression linéaire multiple bayésienne	Régression Lasso
Domaine	Statistique	Apprentissage automatique
Famille	Regression model	Machine learning
Année d'origine	1971	1996
Auteur d'origine	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.	Tibshirani, R.
Type	Bayesian parametric regression	Regularized linear regression (L1 penalty)
Source fondatrice	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Apparentées	6	4
Résumé	Bayesian Multiple Linear Regression models a continuous outcome as a linear combination of several predictors, but instead of producing a single point estimate it yields a full posterior distribution over all regression coefficients and the error variance. This makes uncertainty quantification explicit and allows seamlessly incorporating prior knowledge from theory or previous studies.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.