Comparer des méthodes

	Bayesian Probit model	Régression logistique
Domaine	Statistique	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	1993	1958
Auteur d'origine	Albert & Chib (data augmentation formulation)	David Roxbee Cox
Type	Binary regression (Bayesian)	Method
Source fondatrice	Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669-679.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit	logit model, binomial logistic regression, LR
Apparentées	6	3
Résumé	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.