Lecture
This lecture introduces the concept of mixtures, which are a convex combination of probability distribution functions. It covers both discrete and continuous mixtures, explaining how they are constructed and their properties. The lecture also explores examples of mixtures using different probability distributions and discusses the combination of probit and logit models in choice modeling. Additionally, it addresses the challenge of calculating complex integrals and presents Monte-Carlo integration as a solution.