Bayesian Networks

Lecture Slides

  1. Introduction
  2. Uncertainty in Knowledge Based Systems
  3. Basics of Applied Probability Theory
  4. Decomposible Models
  5. Separation Concepts
  6. Probabilistic Graphical Models
  7. Inference in Belief Trees
  8. Inference in Bayes Networks
  9. Manual Building of Bayes Networks
  10. Building Bayes Networks: Parameter Learning
  11. Building Bayes Networks: Structure Learning
  12. Decision Graphs / Influence Diagrams
  13. Dempster Shafer Theory and Belief Functions
  14. All Slides


  1. Combinatorics, Probabilities
  2. Conditional Probabilities, Stochastic Independency, Bayesian Theorem
  3. Separation Criteria
  4. Marginal Distributions, (conditional) Independencies, Decompositions of Relations
  5. Bayesian Networks, Constructing Bayesian Networks, Conditional Independence
  6. Semi-Graphoid and Graphoid Axioms
  7. Probabilistic Propagation, Construction of Clique Trees, Triangulation and Joint Tree Construction
  8. Clique Tree Propagation
  9. Learning from Data
  10. Markov Properties of Undirected Graphs, Dempster-Shafer Theory


Last Modification: 10.02.2021 - Contact Person: Webmaster