مقایسهٔ روش‌ها

	رگرسیون بیزی	مدل اثرات مختلط (یا مدل خطی مختلط) رگرسیون معمولی را با گنجاندن هر دو اثرات ثابت	رگرسیون ریج (Ridge Regression)
حوزه	بیزی	آمار	یادگیری ماشین
خانواده	Bayesian methods	Regression model	Machine learning
سال پیدایش	—	1982	1970
پدیدآور	—	Laird & Ware	Hoerl, A.E. & Kennard, R.W.
نوع	Bayesian linear model	Mixed effects regression	L2-regularized linear regression
منبع بنیادین	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	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
نام‌های دیگر	bayesian linear regression, probabilistic regression, bayesian regresyon	LME, LMM, mixed model, random effects model	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
مرتبط	2	4	4
خلاصه	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.	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.