Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.
Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Explores Bayesian techniques for extreme value problems, including Markov Chain Monte Carlo and Bayesian inference, emphasizing the importance of prior information and the use of graphs.
