Porovnať metódy

	Bayesovský probitový model	Bayesovská ordinálna logistická regresia
Odbor	Štatistika	Štatistika
Rodina	Regression model	Regression model
Rok vzniku	1993	1999
Tvorca	Albert & Chib (data augmentation formulation)	Johnson & Albert (1999); Bayesian proportional odds framework
Typ	Binary regression (Bayesian)	Bayesian generalized linear model
Pôvodný zdroj	Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669-679.	Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484
Ďalšie názvy	Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit	Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model
Príbuzné	6	6
Zhrnutie	The Bayesian Probit model is a binary regression method that models the probability of a binary outcome using the normal CDF (probit link) within a Bayesian framework. It assigns prior distributions to regression coefficients and updates them with observed data, yielding a full posterior distribution rather than a single point estimate. The Albert-Chib data-augmentation algorithm makes posterior sampling computationally efficient via Gibbs sampling.	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.