A test for a relatively frequent (1:100) condition has 97% specificity and 85% sensitivity. What is the likelihood that a person testing positive has the disease? Few would guess the answer is a little over 20%. Bayes' theorem is a powerful tool to understand causal reasoning, and in particular understand how physicians, epidemiologists, veterinarians and others in the healthcare field reason about the likelihood of a particular diagnosis.
This repo accompanies this blog post (oops... it hasn't been completed yet!) and is intended to illustrate the processes discussed therein.