Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Explores the integration of machine learning into discrete choice models, emphasizing the importance of theory constraints and hybrid modeling approaches.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
