Guest lectures on Bayesian Hierarchical modeling delivered as part of David Hogg's Computational Data Analysis course (2019) at the Flatiron Institute's Center for Computational Astrophysics. These lectures consist of one Keynote presentation and an accompanying jupyter notebook with gaps to completed as part of tasks set during the lecture. I've uploaded a complete version of the notebook so you can see just how much better your solutions are than mine ;).
This code has been tested in Python 2.7.15 and 3.6.8. It requires the following dependencies:
jupyter, numpy, matplotlib, scipy (stats), corner, pystan, pickle.
Running bhm_plot.py also requires daft.