Welcome to STK4290/9290: Probabilistic Graphical Models

The topic of the Spring 2021 version of STK4290/9290 is Probabilistic Graphical Models (PGMs). The course will give an introduction in the PGM framework, which aim at modelling a system over a large number of variables that interact with each other. The PGM framework lies at the intersection of statistics and computer science, combining concepts from probability theory, graph algorithms and machine learning. We will look at the two most basic PGM representations: Bayesian Networks and Markov networks, and cover the main topics related to PGMs: representation, inference, and learning.

As the main text book for the course, we will use:

Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. ISBN-13: 978-0262013192, ISBN-10: 0262013193.