Sammenlign metoder

	Bayesiansk Ordinal Logistisk Regression	Bayesiansk probitmodel
Fagområde	Statistik	Statistik
Familie	Regression model	Regression model
Oprindelsesår	1999	1993
Ophavsperson	Johnson & Albert (1999); Bayesian proportional odds framework	Albert & Chib (data augmentation formulation)
Type	Bayesian generalized linear model	Binary regression (Bayesian)
Oprindelig kilde	Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484	Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669-679.
Aliasser	Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model	Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit
Relaterede	6	6
Resumé	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.	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.