Comparer des méthodes

	Régression logistique ordinale bayésienne	Régression logistique ordinale
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1999	1980
Auteur d'origine	Johnson & Albert (1999); Bayesian proportional odds framework	Peter McCullagh
Type	Bayesian generalized linear model	Ordinal regression / GLM
Source fondatrice	Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484	McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142.
Alias	Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model	proportional-odds model, cumulative link model, ordered logit, OLR
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.	Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds.