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Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.
Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.
