Gaussian Process example with emphasis on length scale priors
Dynamic factors (use tick data); explain how sign-flipping is dealt with
Types of predictions (link, expected, response) and the brms-like wrappers
Hierarchical, time-varying seasonality (hierarchical slopes on fourier series predictors)
Spatiotemporal processes (geostatistical, multiple species, species-level spatial smooths, species-level GPs of time, dynamic factor temporal processes to capture how the spatial fields deviating from the mean spatiotemporal process over time); would highlight how one can fix the observation level params and capture very complex spatiotemporal processes while leveraging data effectively
brms-like wrappers