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
Not necessary
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