Mixture models: summary

This lecture provides a summary of mixtures of logit models, explaining the concept of using building blocks like logit models and convex combinations to create more sophisticated models. It covers continuous and discrete mixing, Monte-Carlo integration, relaxing i.i.d. assumptions, taste heterogeneity, and latent class models. The instructor emphasizes the importance of careful modeling and the systematic part of the utility function.