방법 비교

	베이지안 LASSO 회귀	베이즈 능형 회귀(Bayesian Ridge Regression)
분야	통계학	머신러닝
계열	Regression model	Bayesian methods
기원 연도	2008	1992
창시자	Park & Casella	MacKay, D. J. C.
유형	Bayesian regularized regression	Probabilistic regularised regression
원전	Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681–686.	MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447.
별칭	Bayesian LASSO, Bayesian L1 regression, double-exponential prior regression, Laplace prior regression	BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge
관련	5	3
요약	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 Ridge Regression is a probabilistic formulation of ridge regression, introduced by David J. C. MacKay in 1992, in which the regularisation strength and noise precision are not fixed by the analyst but are instead estimated automatically by maximising the marginal likelihood (evidence) of the observed data. The result is a full posterior distribution over the regression weights together with calibrated predictive uncertainty.