Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
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.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
