Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how ...

This courses introduces causal inference methods, primarily using probabilistic graphical models, to identify and estimate counterfactual quantities as functions of observational data. We will discuss ...

On the 8th of December 2021, M.Sc. Kari Rantanen will defend his doctoral thesis on Optimization Algorithms for Learning Graphical Model Structures. The thesis a part of research done in the ...

A five-minute formula from Alexander Denev that takes you through a simple probabilistic graphical model and explains how and why these are used. Find out more about the ground-breaking book, ...