So sánh phương pháp

	Hồi quy Bayesian LASSO	Hồi quy tuyến tính bội Bayes
Lĩnh vực	Thống kê	Thống kê
Họ	Regression model	Regression model
Năm ra đời	2008	1971
Người khởi xướng	Park & Casella	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.
Loại	Bayesian regularized regression	Bayesian parametric regression
Công trình gốc	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
Tên gọi khác	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
Liên quan	5	6
Tóm tắt	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.