389.111 Graphical models in signal processing and communications
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, VO, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VO Lecture

Learning outcomes

After successful completion of the course, students are able to apply methods from the areas of probabilistic graphical models and graph signal processing to practical engineering problems; this comprises the problem formulation, the analytical or numerical solution, and the qualitative and quantitative performance characterization.

Subject of course

  • fundamentals of probability and graph theory
  • applications
  • types of graphical models (Bayesian networks, Markov random fields, factor graphs, ...)
  • methods and algorithms for inference on graphs
    • message passing, belief propagation
    • variational methods
  • Graph signal processing
    • graph shift and graph Fourier transform
    • graph filters
    • graph signal recovery
    • graph learning
    • clustering

Teaching methods

Conventional lecture on the blackboard supported by electronic media.

Mode of examination


Additional information

All lectures are held on Tuesday 1pm via Zoom:


Recordings of the lectures are available on the following TUpeerTube channel:


All other materials (slides, scans, etc.) are available on TISS.



Course dates

Tue13:00 - 15:0003.03.2020 - 10.03.2020Sem 389 Vorlesung
Graphical models in signal processing and communications - Single appointments
Tue03.03.202013:00 - 15:00Sem 389 Vorlesung
Tue10.03.202013:00 - 15:00Sem 389 Vorlesung

Examination modalities

oral exam

Course registration

Not necessary



Lecture notes are available.

Further References:

  • Daphne Koller and Nir Friedman, "Probabilistic Graphical Models", MIT Press 2009
  • Michael Jordan (Ed.), "Learning in Graphical Models", Kluwer 1998
  • Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer 2006
  • Petar Djuric and Cedric Richard (Eds.), "Cooperative and Graph Signal Processing", Elsevier 2018

Previous knowledge

probability theory and random variables