Authors: Danne Elbers, Thomas Wiecki

Today's blog post is co-written by Danne Elbers who is doing her masters thesis with me on computational psychiatry using Bayesian modeling. This post also borrows heavily from a Notebook by Chris Fonnesbeck.

The power of Bayesian modelling really clicked for me when I was first introduced to hierarchical modelling. In this blog post we will:

• Provide and intuitive explanation of hierarchical/multi-level Bayesian modeling;
• Show how this type of model can easily be built and estimated in PyMC3;
• Demonstrate the advantage of using hierarchical Bayesian modelling as opposed to non-hierarchical Bayesian modelling by comparing the two;
• Visualize the "shrinkage effect" (explained below); and
• Highlight connections to the frequentist version of this model.