手法を比較

	ベイズ順序ロジスティック回帰	多項ロジスティック回帰
分野	統計学	統計学
系統	Regression model	Regression model
提唱年	1999	1966–1974
提唱者	Johnson & Albert (1999); Bayesian proportional odds framework	Cox (1966); Theil (1969); formalized by McFadden (1974)
種類	Bayesian generalized linear model	Generalized linear model
原典	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
別名	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
関連	6	4
概要	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.