Comparer des méthodes

	Régression LASSO Bayésienne	Régression linéaire multiple bayésienne
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2008	1971
Auteur d'origine	Park & Casella	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.
Type	Bayesian regularized regression	Bayesian parametric regression
Source fondatrice	Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681–686.	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
Alias	Bayesian LASSO, Bayesian L1 regression, double-exponential prior regression, Laplace prior regression	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression
Apparentées	5	6
Résumé	Bayesian LASSO regression places double-exponential (Laplace) priors on regression coefficients, which is the Bayesian analogue of the classical LASSO penalty. It simultaneously shrinks small coefficients toward zero and performs soft variable selection, all within a coherent posterior inference framework that naturally quantifies parameter uncertainty through credible intervals.	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.