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.

2023S, VO, 2.0h, 3.0EC


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

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, 3 March 2023 at 3.00 pm in seminar room 389 (room CG 01 18).




Course dates

Fri15:00 - 17:0003.03.2023 - 30.06.2023Sem 389 Vorlesung
Fri15:00 - 17:0007.07.2023Sem 389 Vorlesung
Parameter Estimation Methods - Single appointments
Fri03.03.202315:00 - 17:00Sem 389 Vorlesung
Fri10.03.202315:00 - 17:00Sem 389 Vorlesung
Fri17.03.202315:00 - 17:00Sem 389 Vorlesung
Fri24.03.202315:00 - 17:00Sem 389 Vorlesung
Fri31.03.202315:00 - 17:00Sem 389 Vorlesung
Fri21.04.202315:00 - 17:00Sem 389 Vorlesung
Fri28.04.202315:00 - 17:00Sem 389 Vorlesung
Fri05.05.202315:00 - 17:00Sem 389 Vorlesung
Fri12.05.202315:00 - 17:00Sem 389 Vorlesung
Fri26.05.202315:00 - 17:00Sem 389 Vorlesung
Fri02.06.202315:00 - 17:00Sem 389 Vorlesung
Fri09.06.202315:00 - 17:00Sem 389 Vorlesung
Fri16.06.202315:00 - 17:00Sem 389 Vorlesung
Fri23.06.202315:00 - 17:00Sem 389 Vorlesung
Fri30.06.202315:00 - 17:00Sem 389 Vorlesung
Fri07.07.202315:00 - 17:00Sem 389 Vorlesung

Examination modalities

Oral exam

Course registration

Not necessary


Study CodeObligationSemesterPrecon.Info
066 507 Telecommunications Not specified
710 FW Elective Courses - Electrical Engineering Elective


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