Kursinformation

(This is the moodle course for the lab course. The seminar of the same name can be found here.)

The Bayesian perspective on probability is a cornerstone of modern applied statistics and probabilistic machine learning. Probabilistic models formulated in this framework allow to explicitly communicate and challenge assumptions, perform consistent reasoning, and quantify the uncertainty of predictions — they are a useful tool in data-driven research as well as decision-making.

This lab course teaches how such models can be implemented in Python — using commonly used modeling packages like Scipy and Pyro — and fit to data for Bayesian inference, uncertainty quantification and prediction.