方法对比

	贝叶斯LASSO回归	岭回归（Ridge Regression）
领域	统计学	机器学习
方法族	Regression model	Machine learning
起源年份	2008	1970
提出者	Park & Casella	Hoerl, A.E. & Kennard, R.W.
类型	Bayesian regularized regression	L2-regularized linear regression
开创性文献	Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681–686.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
别名	Bayesian LASSO, Bayesian L1 regression, double-exponential prior regression, Laplace prior regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
相关	5	4
摘要	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.