Probabilistic Graphical Models

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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.

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

Author: Denis D. Mauá

Created: 2018-11-29 Thu 12:02

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