- Intro to Stan, including:
- Coding linear regression to assess wine quality
- Demonstrating important parts of the Stan program
- Doing some basic posterior predicting checking
- Introduction to calibration and model comparison
- Introduction to making decisions with Bayesian models
- From classical GLMs to multi-level model
- Comparing classical GLMs with bayesian GLMs using rstanarm
- Building more complex multi-level models using brms
- Examples from pricing and claims reserving
- Case studies from the insurance industry
- Loss development curves in Stan (Mick Cooney)
- Hierarchical compartmental reserving models (Jake Morris)
- Work on your own problems or work through on of the following examples: