"Our golems rarely have a physical form, but they too are often made of clay living in silicon as computer code."
- Richard McElreath
Now that we have a basic understanding of Bayesian statistics, we are going to learn how to build probabilistic models using computational tools. Specifically, we are going to learn about probabilistic programming with PyMC3. The basic idea is to specify models using code and then solve them in a more or less automatic way. It is not that we are too lazy to learn the mathematical way, nor are we elitist-hardcore-hackers-in-code. One important reason behind this choice is that many models do not lead to an analytic closed form, and thus we can only solve those models using numerical techniques.
Another reason to learn probabilistic programming is that modern Bayesian statistics is mainly done by writing code...
