পদ্ধতির তুলনা করুন

	বেয়েশীয় বহুচলক রৈখিক রিগ্রেশন (Bayesian Multiple Linear Regression)	রিজ রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	যন্ত্র শিখন
পরিবার	Regression model	Machine learning
উদ্ভবের বছর	1971	1970
প্রবর্তক	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.	Hoerl, A.E. & Kennard, R.W.
ধরন	Bayesian parametric regression	L2-regularized linear regression
মৌলিক উৎস	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.
অপর নাম	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
সম্পর্কিত	6	4
সারসংক্ষেপ	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.