Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Covers the concept of distribution estimation and the optimization of parameters using different estimators.
Covers t-tests, p-values calculation, and comparison of coefficients.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
