Bayesian Analysis Recipes

I once saw a probability estimate with mean 0.8 and variance 0.3. From that point onwards, I knew frequentist estimates could be horribly wrong, and decided to go Bayesian.

As part of my learning journey, I decided to make publicly available a GitHub repository of Bayesian statistical analysis recipes in PyMC3 featuring models and data that I've seen elsewhere. Most of them I implemented from scratch, to get familiar with PyMC3 syntax and to get familiar with the logic of Bayesian statistical modelling.

Some models that are implemented here include:

  • Binary and multinomial classification.
  • Neural networks
  • Linear regression
  • Hierarchical modelling

The GitHub repository can be found here.