Сравнение методов

	Байесовская ординарная логистическая регрессия	Байесовская логистическая регрессия
Область	Статистика	Байесовские методы
Семейство	Regression model	Bayesian methods
Год появления	1999	2008
Автор метода	Johnson & Albert (1999); Bayesian proportional odds framework	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)
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., Jakulin, A., Pittau, M. G. & Su, Y.-S. (2008). A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models. Annals of Applied Statistics, 2(4), 1360–1383.
Другие названия	Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model	bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon
Связанные	6	3
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 logistic regression is a classification model that applies Bayesian inference to a logistic (sigmoid) likelihood for binary or multinomial outcomes. Developed within the weakly-informative prior framework formalised by Gelman, Jakulin, Pittau and Su (2008), it places a prior distribution over the coefficients and combines that prior with the data likelihood to yield a full posterior distribution for each parameter — delivering calibrated class probabilities and honest uncertainty even in small samples, rare-event settings, or cases of complete separation where frequentist maximum likelihood estimation collapses.