I'll provide an end-to-end example of using R and Stan to carry out full Bayesian inference for a simple set of repeated binary trial data: Efron and Morris's classic baseball batting data, with multiple players observed for many at bats; clinical trial, educational testing, and manufacturing quality control problems have the same flavor.

We will consider three models that provide complete pooling (every player is the same), no pooling (every player is independent), and partial pooling (every player is to some degree like every other player). Hierarchical models allow the degree of similarity to be jointly modeled with individual effects, tightening estimates and sharpening predictions compared to the no pooling and complete pooling models. They also outperform empirical Bayes and max marginal likelihood predictively, both of which rely on point estimates of hierarchical parameters (aka "mixed effects").

I'll show how to fit observed data to make predictions for future observations, estimate event probabilities, and carry out (multiple) comparisons such as ranking. I'll explain how hierarchical modeling mitigates the multiple comparison problem by partial pooling (and I'll tie it into rookie of the year effects and sophomore slumps). Along the way, I will show how to evaluate models predictively, preferring those that are well calibrated and make sharp predictions. I'll also show how to evaluate model fit to data with posterior predictive checks and Bayesian p-values.

### Speaker(s)

### Bob Carpenter

Columbia University

Bob Carpenter is a research scientist in computational statistics at Columbia University. He designed the Stan probabilistic programming language and is one of the Stan core developers. Before that, he was an industry research scientist and programmer (LingPipe, SpeechWorks, Bell Labs) and a professor in natural language processing and linguistics (Carnegie Mellon). He has a Ph.D. in cognitive and computer science (University of Edinburgh).