Salīdzināt metodes

	Bayesas daudzkārtējā lineārā regresija	Regulētā lineārā regresija (Ridge Regression)
Nozare	Statistika	Mašīnmācīšanās
Saime	Regression model	Machine learning
Izcelsmes gads	1971	1970
Autors	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.	Hoerl, A.E. & Kennard, R.W.
Tips	Bayesian parametric regression	L2-regularized linear regression
Pirmavots	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Citi nosaukumi	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Saistītās	6	4
Kopsavilkums	Bayesian Multiple Linear Regression models a continuous outcome as a linear combination of several predictors, but instead of producing a single point estimate it yields a full posterior distribution over all regression coefficients and the error variance. This makes uncertainty quantification explicit and allows seamlessly incorporating prior knowledge from theory or previous studies.	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.