Probabilistic Graphical Models

Denis Deratani Mauá

equiv.png

These are the lecture notes of the Graduate Course MAC6916 Probabilistic Graphical Models, taught within the Computer Science Program of the Institute of Mathematics & Statistics since 2016. These notes are preliminary and in constant change (use at your own risk ☺). Comments and error reports are very welcome.

  1. Preamble: Introduction to Probabilistic Graphical Reasoning
  2. Probability Theory
  3. Bayesian Networks
  4. Representing Independences
  5. Missingness Graphs
  6. Markov Equivalence
  7. Markov Networks
  8. Constraint-Based Structure Learning
  9. Parameter Learning
  10. Score-Based Structure Learning
  11. Probabilistic Inference By Sampling
  12. Variable Elimination
  13. Sum-Product Algorithms
  14. Bayesian Networks Classifiers
  15. Causality