389.119 Parameter Estimation Methods
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2022S, VO, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VO Lecture
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to recognize parameter estimation problems that occur in engineering and science, and to solve them by applying standard methods. A further outcome is an improvement of English language skills.

Subject of course

* Introduction: Motivation, applications, survey, history.

* Deterministic parameter estimation methods: Least squares and variations.

* Bayesian statistical estimation methods: General theory, Bayesian Cramér-Rao bound, MAP and minimum mean square; applications: linear prediction, Wiener filter and Kalman filter, system identification.

* Classical statistical estimation methods: Method of moments, maximum likelihood, EM algorithm, MVU estimators, BLUE, Cramér-Rao bound.

Teaching methods

The prof (Hlawatsch) verbally presents the class material, discusses the material with his students, and answers the students' questions. For this, he uses a blackboard, on which he writes certain characters and draws simple figures with a piece of chalk (also using different colors if helpful). He also uses a tablecloth to erase the board every now and then. Finally, he uses an overhead projector to project more complicated figures and tables on a screen. The prof's presentation is supported by detailed lecture notes, which, however, do not contain most of the examples and problems discussed in class.

Mode of examination


Additional information

This course is an optional part of the "Wahlmodul Advanced Signal Processing."

First class: Friday, 4 March 2022 at 3.00 pm in seminar room 389 (also known as 118).




Course dates

Fri14:30 - 17:0004.03.2022 - 24.06.2022Sem 389 Vorlesung
Parameter Estimation Methods - Single appointments
Fri04.03.202214:30 - 17:00Sem 389 Vorlesung
Fri11.03.202214:30 - 17:00Sem 389 Vorlesung
Fri18.03.202214:30 - 17:00Sem 389 Vorlesung
Fri25.03.202214:30 - 17:00Sem 389 Vorlesung
Fri01.04.202214:30 - 17:00Sem 389 Vorlesung
Fri08.04.202214:30 - 17:00Sem 389 Vorlesung
Fri29.04.202214:30 - 17:00Sem 389 Vorlesung
Fri06.05.202214:30 - 17:00Sem 389 Vorlesung
Fri13.05.202214:30 - 17:00Sem 389 Vorlesung
Fri20.05.202214:30 - 17:00Sem 389 Vorlesung
Fri03.06.202214:30 - 17:00Sem 389 Vorlesung
Fri10.06.202214:30 - 17:00Sem 389 Vorlesung
Fri17.06.202214:30 - 17:00Sem 389 Vorlesung
Fri24.06.202214:30 - 17:00Sem 389 Vorlesung

Examination modalities

Oral exam

Course registration

Not necessary



Lecture notes for this course are available at the "Grafisches Zentrum" of TU Wien, Wiedner Hauptstraße 8-10, 1040 Vienna.

Recommended textbook: S.M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice-Hall, 1993.

Previous knowledge

Working knowledge of random variables and linear algebra