Comparer des méthodes

	Régression logistique multinomiale bayésienne	Multinomial Logistic Regression
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1966 (classical); Bayesian extensions established by 1990s	1966–1974
Auteur d'origine	Gelman et al. (Bayesian treatment); classical multinomial logit by Cox (1966)	Cox (1966); Theil (1969); formalized by McFadden (1974)
Type	Bayesian classification model	Generalized linear 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	Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933
Alias	Bayesian polytomous logistic regression, Bayesian multinomial logit, Bayesian softmax regression, Bayesian nominal logistic regression	polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression
Apparentées	5	4
Résumé	Bayesian Multinomial Logistic Regression models a nominal outcome with three or more unordered categories by placing prior distributions over the regression coefficients and updating them with data via Bayes' theorem. The result is a full posterior distribution over category probabilities for each observation, enabling principled uncertainty quantification and regularization through the prior.	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.