"The first principle is that you must not fool yourself—and you are the easiest person to fool."
- Richard Feynman
So far, we have focused on model building, interpretation of results and criticism of models. We have relied on the magic of the pm.sample function to compute the posterior distributions for us. Now we will focus on learning some of the details of the inference engines behind this function. The whole purpose of probabilistic programming tools, such as PyMC3, is that the user should not care about how sampling is carried out, but understanding how we get samples from the posterior is important for a full understanding of the inference process, and could also help us to get an idea of when and how these methods fail and what to do about it. If you are not interested in understanding how the methods for approximating the posterior works...
