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	Bayesowska regresja grzbietowa	Regularyzacja grzbietowa (Ridge Regression)
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Bayesian methods	Machine learning
Rok powstania	1992	1970
Twórca	MacKay, D. J. C.	Hoerl, A.E. & Kennard, R.W.
Typ	Probabilistic regularised regression	L2-regularized linear regression
Źródło pierwotne	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.
Inne nazwy	BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Pokrewne	3	4
Podsumowanie	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.