What: A 3 hour course on hierarchical models.
Who: The instructor is Sean Pinkney
Agenda: Background on hierarchical models, partial pooling and reparameterizations, and examples with meta-analysis, car insurance, and advertising.
New stuff for people who think they know hierarchical models: A derivation of a prior for partially centered parameters (i.e. where the weight is between 0 and 1), location-scale meta-analysis that shows the power of Bayesian models against frequentist, a data driven prior for marginalizing out id-level hierarchy when you want Bayes to go vroom.
Pre-reqs: Basic familiarity with Stan, Stan setup on computer (e.g. cmdstanr/cmdstanpy), and basic statistics.