Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
Explains the derivation of the logit model in choice models, covering error terms, choice sets, and availability conditions.
Summarizes mixtures of logit models, covering various mixing methods and modeling techniques for taste heterogeneity.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
