Comparer des méthodes

	Régression linéaire simple bayésienne	Modèle bayésien linéaire généralisé
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	Early 19th century; textbook synthesis 2013	1989 (GLM); 1995 (Bayesian BDA)
Auteur d'origine	Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Type	Bayesian linear regression	Bayesian regression model
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	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 SLR, Bayesian univariate regression, probabilistic simple linear regression, Bayesian linear model	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Apparentées	6	6
Résumé	Bayesian Simple Linear Regression models the relationship between a continuous outcome and a single predictor by combining a Gaussian likelihood with prior distributions over the intercept, slope, and error variance. The result is a full posterior distribution over all parameters, providing probabilistic uncertainty quantification rather than a single point estimate.	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.