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	Bayesian OLS	Regularyzacja grzbietowa (Ridge Regression)
Dziedzina	Ekonometria	Uczenie maszynowe
Rodzina	Regression model	Machine learning
Rok powstania	1971	1970
Twórca	Arnold Zellner	Hoerl, A.E. & Kennard, R.W.
Typ	Bayesian linear regression	L2-regularized linear regression
Źródło pierwotne	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Inne nazwy	Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Pokrewne	5	4
Podsumowanie	Bayesian OLS combines the classical linear regression likelihood with prior distributions over the coefficients and error variance. Rather than reporting point estimates, it produces full posterior distributions that quantify both estimated effects and their uncertainty. The approach is especially valuable when prior knowledge is available or when samples are small.	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.