# Probabilistic Graphical Models

These are the lectures notes of the Graduate Course Probabilistic Graphical Models, taught within the Computer Science Program of the Institute of Mathematics & Statistics since 2016. These notes are in constant change (use at your own risk :) ). Comments and error reports are very welcome.

- Preamble: Introduction to Probabilistic Graphical Reasoning
- Probability Theory
- Bayesian Networks
- Representing Independences
- Markov Equivalence
- Markov Networks
- Constraint-Based Structure Learning
- Parameter Learning
- Score-Based Structure Learning
- Probabilistic Inference By Sampling
- Variable Elimination
- Sum-Product Algorithms
- Bayesian Networks Classifiers
- Causality