Comparer des méthodes

	Régression bayésienne	Modèle à effets mixtes
Domaine	Bayésien	Statistique
Famille	Bayesian methods	Regression model
Année d'origine	—	1982
Auteur d'origine	—	Laird & Ware
Type	Bayesian linear model	Mixed effects regression
Source fondatrice	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.
Alias	bayesian linear regression, probabilistic regression, bayesian regresyon	LME, LMM, mixed model, random effects model
Apparentées	2	4
Résumé	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.