Comparer des méthodes

	Modèle bayésien à effets mixtes	Modèle bayésien linéaire généralisé
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1990s–2000s (modern Bayesian MCMC era)	1989 (GLM); 1995 (Bayesian BDA)
Auteur d'origine	Gelman, Hill, and the broader Bayesian hierarchical modeling tradition	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Type	Bayesian regression model	Bayesian regression model
Source fondatrice	Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891	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
Alias	Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Apparentées	5	6
Résumé	The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for both population-level and group-level effects simultaneously.	A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome.