## Chapter 10. Bayesian Inference and Probabilistic Programming

Mathematics is a big space of which humans so far have only charted a small amount. We know of countless areas in mathematics that we would like to visit, but that are not tractable computationally.

A prime reason Newtonian physics, as well as much of quantitative finance, is built around elegant but oversimplified models is that these models are easy to compute. For centuries, mathematicians have mapped small paths in the mathematical universe that they could travel down with a pen and paper. However, this all changed with the advent of modern high-performance computing. It unlocked the ability for us to explore wider spaces of mathematics and thus gain more accurate models.

In the final chapter of this book, you'll learn about the following:

The empirical derivation of the Bayes formula

How and why the Markov Chain Monte Carlo works

How to use PyMC3 for Bayesian inference and probabilistic programming

How various methods get applied...