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.
* 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.
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.
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).
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.
