Krahasoni metodat

	Modeli Logit i Përzier	Regresioni Bajesian	Regresioni logjik multinomial
Fusha	Ekonometri	Statistika bajesiane	Ekonometri
Familja	Regression model	Bayesian methods	Regression model
Viti i origjinës	2000	—	1974
Krijuesi	Daniel McFadden & Kenneth Train	—	McFadden
Lloji	Random-parameters discrete choice model	Bayesian linear model	Multinomial logistic regression
Burimi themelues	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	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503
Emërtime të tjera	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	bayesian linear regression, probabilistic regression, bayesian regresyon	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon
Të lidhura	3	2	5
Përmbledhja	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.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.