# 389.119 Parameter Estimation Methods This course is in all assigned curricula part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_21",{id:"j_id_21",showEffect:"fade",hideEffect:"fade",target:"isAllSteop"});});This course is in at least 1 assigned curriculum part of the STEOP.\$(function(){PrimeFaces.cw("Tooltip","widget_j_id_23",{id:"j_id_23",showEffect:"fade",hideEffect:"fade",target:"isAnySteop"});}); 2024S 2023S 2022S 2021S 2020S 2019S 2018S 2017S 2016S 2015S 2014S 2013S 2012S 2011S 2009S 2007W

2023S, VO, 2.0h, 3.0EC

## Properties

• 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

Oral

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

DayTimeDateLocationDescription
Fri15:00 - 17:0003.03.2023 - 30.06.2023Sem 389 Vorlesung
Fri15:00 - 17:0007.07.2023Sem 389 Vorlesung
Parameter Estimation Methods - Single appointments
DayDateTimeLocationDescription
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

Oral exam

Not necessary

## Curricula

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

## Literature

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

English