Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.
Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.
Explores the nested logit model for discrete choice and its implications on choice behavior and parameter estimation.
