Comparar métodos

	Modelo Logit Mixto	Regresión bayesiana	Modelo de Elección Discreta Logit Anidado
Campo	Econometría	Bayesiano	Econometría
Familia	Regression model	Bayesian methods	Regression model
Año de origen	2000	—	1985
Autor original	Daniel McFadden & Kenneth Train	—	Daniel McFadden; Ben-Akiva & Lerman
Tipo	Random-parameters discrete choice model	Bayesian linear model	Discrete choice regression model
Fuente seminal	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	Ben-Akiva, M., & Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press. ISBN: 978-0-262-02217-0
Alias	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	bayesian linear regression, probabilistic regression, bayesian regresyon	Tree Logit Model, Hierarchical Logit Model, Generalized Extreme Value Logit, İç İçe Logit Modeli
Relacionados	3	2	3
Resumen	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.	The Nested Logit model is a discrete choice framework that groups mutually exclusive alternatives into hierarchical nests, allowing correlated unobserved utilities within each nest while maintaining independence across nests. Introduced formally by Ben-Akiva and Lerman (1985) and grounded in McFadden's Generalized Extreme Value (GEV) theory, it extends the standard Multinomial Logit by relaxing the restrictive Independence of Irrelevant Alternatives assumption within predefined groups of similar alternatives.