Comparer des méthodes

	Régression logistique ordinale bayésienne	Modèle bayésien linéaire généralisé
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1999	1989 (GLM); 1995 (Bayesian BDA)
Auteur d'origine	Johnson & Albert (1999); Bayesian proportional odds framework	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Type	Bayesian generalized linear model	Bayesian regression model
Source fondatrice	Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484	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 proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Apparentées	6	6
Résumé	Bayesian ordinal logistic regression extends the classical proportional odds model by placing prior distributions on the regression coefficients and threshold parameters and updating them with observed data via Bayes' theorem. The result is a full posterior distribution over all parameters, enabling uncertainty quantification without relying on large-sample approximations.	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.