Сравнение методов

	Байесовская гребневая регрессия	Гребневая регрессия
Область	Машинное обучение	Машинное обучение
Семейство	Bayesian methods	Machine learning
Год появления	1992	1970
Автор метода	MacKay, D. J. C.	Hoerl, A.E. & Kennard, R.W.
Тип	Probabilistic regularised regression	L2-regularized linear regression
Основополагающий источник	MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Другие названия	BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Связанные	3	4
Сводка	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.	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.