Comparer des méthodes

	Régression logistique ordinale bayésienne	Multinomial Logistic Regression
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1999	1966–1974
Auteur d'origine	Johnson & Albert (1999); Bayesian proportional odds framework	Cox (1966); Theil (1969); formalized by McFadden (1974)
Type	Bayesian generalized linear model	Generalized linear model
Source fondatrice	Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484	Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933
Alias	Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model	polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression
Apparentées	6	4
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.	Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels.