Abstract

I will introduce some of the basic technical notions of Bayesian learning and inference, including model fitting, model selection, and model averaging from a hierarchical Bayesian viewpoint. The treatment will be mathematical but not in-depth or heavily rigorous. I will emphasize simple examples that make the principles intuitive and demonstrate their applicability to modeling human cognitive inferences. All of these topics will be treated in more depth and rigor by later lectures this week.