Сравнение методов

	Байесовская ординарная логистическая регрессия	Байесовская мультиномиальная логистическая регрессия
Область	Статистика	Статистика
Семейство	Regression model	Regression model
Год появления	1999	1966 (classical); Bayesian extensions established by 1990s
Автор метода	Johnson & Albert (1999); Bayesian proportional odds framework	Gelman et al. (Bayesian treatment); classical multinomial logit by Cox (1966)
Тип	Bayesian generalized linear model	Bayesian classification model
Основополагающий источник	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
Другие названия	Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model	Bayesian polytomous logistic regression, Bayesian multinomial logit, Bayesian softmax regression, Bayesian nominal logistic regression
Связанные	6	5
Сводка	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.	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.