Confronta i metodi

	Modello Probit Bayesiano	Regressione logistica bayesiana
Campo	Statistica	Bayesiano
Famiglia	Regression model	Bayesian methods
Anno di origine	1993	2008
Ideatore	Albert & Chib (data augmentation formulation)	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)
Tipo	Binary regression (Bayesian)	Bayesian classification model
Fonte seminale	Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669-679.	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.
Alias	Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit	bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon
Correlati	6	3
Sintesi	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 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.