Bayesian Inference in the Multinomial Logit Model
نویسندگان
چکیده
The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model. Zusammenfassung: Das multinomiale logistische (MNL) Regressionsmodell besitzt eine latente Variablendarstellung, die einen zufälligen Fehlerterm beinhaltet, der einer multivariaten logistischen Verteilung folgt. Aufbauend auf einer finiten Mischungsapproximation der multivariaten logistischen Verteilung werden mehrere Metropolis-Hastings-Verfahren für eine Bayes-Analyse im MNL Regressionsmodell entwickelt.
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