The “learn X in 20min” series seems to be a popular amongst the viewers, so Ellis Hughes and I decided to throw together one. This week, we are walking through the basic code for a Bayesian regression model using the {rstanarm} package. Because we only keep it to 20min, we use the default priors and don’t get into any sort of complex stuff about Bayes. We do, however, discuss making predictions of point estimates and posterior distributions, and show how you can explore model uncertainty.
To watch the screen cast, CLICK HERE.
To access the code, CLICK HERE.