Confronta i metodi

	Regressione bayesiana LASSO	Regressione Lineare Multipla Bayesiana
Campo	Statistica	Statistica
Famiglia	Regression model	Regression model
Anno di origine	2008	1971
Ideatore	Park & Casella	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.
Tipo	Bayesian regularized regression	Bayesian parametric regression
Fonte seminale	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
Correlati	5	6
Sintesi	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.