Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.
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.
Delves into the logit model's derivation, emphasizing the importance of the independence assumption and parameter normalization during estimation.
Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Explores astrophysical fluids, plasmas, MHD, turbulence, and plasma oscillations, including Faraday rotation for measuring cosmic magnetic fields.
