Chapter 2
Programming Probabilistically

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 very basic understanding of probability theory and 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 PyMC [Abril-Pla et al., 2023]. The basic idea is that we use code to specify statistical models and then PyMC will solve those models for us. We will not need to write Bayes’ theorem in explicit form. This is a good strategy for two reasons. First, many models do not lead to an analytic closed form, and thus we can only solve those models using numerical techniques. Second, modern Bayesian statistics is mainly done by writing code. We will be able to see that probabilistic programming offers an effective way to build and solve complex models and...