Ability to recognize parameter estimation problems that occur in engineering and science, and to solve them by applying standard methods. 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.

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

First class:

date and time: Friday, 8 March 2019, 3.00 pm

place: seminar room SEM 389 (formerly 118), Institute of Telecommunications

Oral exam

Lecture notes for this course are available at the "Graphisches Zentrum" of Vienna University of Technology, Wiedner Hauptstraße 8-10, 1040 Vienna.

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

Working knowledge of random variables and linear algebra