Machine learning models can be pretty impressive — sometimes almost magical — when you see them in action. They can diagnose diseases, control robots, or even help scientists design new materials, as shown in some recent examples. But they’re not perfect. Every now and then, a model behaves in strange ways. That’s why it’s worth asking not only what a model predicts, but how confident it is about those predictions.

This is where probabilistic machine learning comes in. Instead of giving just one answer, these models tell us a range of possible answers along with how likely each one is. One of the most powerful and elegant tools for this is the Gaussian Process (GPs).