Stan is a Bayesian modeling language that enjoys wide adoption across industry and science due to its ability to model complex phenomenon, offer human interpretable simulations and capture uncertainty in an arguably idea way for artificial intelligence and descriptive statistics.

This class is for those who don’t know statistics, extremely rusty with statistics or just want a gentle introduction to Bayesian modeling the Stan way. Most importantly we get you through the awkward ‘no idea phase’ of learning a new technology to having a basic understanding of how to work with the software. We will cover the mechanics of how Stan programs work, show simple Bayesian models and posteriors. We presuppose that you are comfortable with general programming concepts like subroutines and variable assignment. We will briefly cover Python interfaces to Stan but the majority of the class will be using pure Stan from the command line using a text editor.

Please see the Pre/Post test at: https://forms.gle/e3USWaBRsmuz4PDo7 to get a more detailed idea of what we are covering.

We will likely have cloud instances of Stan available but those that are comfortable with git should install: https://github.com/stan-dev/cmdstan/wiki/Getting-Started-with-CmdStan