The world isn’t black and white; it’s Bayesian. These statistical models have a solid mathematical foundation to not only give a clear cut result, but also return the confidence in that result. This makes the models easy to interpret and incorporate in real-world decision making. And that’s not all: with some domain expertise, a Bayesian model will also perform well on small, noisy datasets. It should come as no surprise that Bayesian statistics are applied in many different fields, from e-commerce to healthcare.
During this course, you will learn how to think Bayesian: you will apply Bayesian statistics to a number of practical use cases, and learn about various relevant concepts in the process. MCMC-methods, mixture models, and partial pooling will all make an appearance during this course, however, the focus is practical rather than theoretical. At the end of the course, you’ll be able to build Bayesian models using PyMC3.