Introduction to Bayesian Modeling using Stan
By the end of the course, participants should be able to:
- Understand the syntax of Stan and the high-level ideas behind MCMC and HMC.
- Fit standard models (such as linear models) in Stan.
- Understand how hierarchical modeling works, and be able to fit complex hierarchical models in different settings.
- Carry out sensitivity analyses to investigate how posteriors change as a result of prior specification.
- Visualize and interpret different models.
- Carry out posterior predictive checks and cross-validation for model evaluation.
We will provide lecture notes and suggested readings for further study. We assume that everyone has a laptop with them and has the R package rstan installed within R.
This one-day workshop will involve lectures interspersed with short exercises to be done in class. In order to consolidate understanding, we will assign a project that participants can carry out (this is optional). Students have the option to submit it to the instructor a week later and get feedback.
More information on the workshop are provided here: http://www.ling.uni-potsdam.