Comparer des méthodes

	Modèle Logit Mixte	Régression bayésienne
Domaine	Économétrie	Bayésien
Famille	Regression model	Bayesian methods
Année d'origine	2000	—
Auteur d'origine	Daniel McFadden & Kenneth Train	—
Type	Random-parameters discrete choice model	Bayesian linear model
Source fondatrice	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Alias	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	bayesian linear regression, probabilistic regression, bayesian regresyon
Apparentées	3	2
Résumé	The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.