# EQ2810 Estimeringsteori, forskarförberedande6,0 hp

## Estimation Theory, Accelerated Program Course

This is an introductory course to statistical estimation theory given from a signal processing perspective. The course covers fundamental concepts such as sufficient statistics, the Rao-Blackwell theorem and the Cramer-Rao lower bound on estimation accuracy. Furthermore, the most common estimation methods are treated, including maximum likelihood, least-squares, minimum variance and Bayesian estimation.

This is a graduate level course that can be taken by undergraduate students who are admitted. There are two versions of the course, 6 and 12 ECTS.

• #### Huvudområde

Elektroteknik
• #### Betygsskala

A, B, C, D, E, FX, F

## Kurstillfällen/kursomgångar

### HT19 för programstuderande

• #### Perioder

HT19 P1 (6,0 hp)

50694

2019-08-26

2019-10-25

Engelska

KTH Campus

Dagtid

Normal

• #### Antal platser

Ingen begränsning

P1: J2. mer info

• #### Kursansvarig

Magnus Jansson <janssonm@kth.se>

## Lärandemål

This is an introductory course to statistical estimation theory given from a signal processing perspective. The aim is to provide the basic principles and tools which are useful to solve many estimation problems in signal processing and communications. It will also serve as the necessary prerequisite for more advanced texts and research papers in the area. The course will cover fundamental concepts such as sufficient statistics, the Rao-Blackwell theorem and the Cramer-Rao lower bound on estimation accuracy. Furthermore, the most common estimation methods are treated, including maximum likelihood, least-squares, minimum variance, method of moments and Bayesian estimation. The course assumes some familiarity with basic matrix theory and statistics.

## Kursens huvudsakliga innehåll

Introduction, minimum variance estimation, Cramer-Rao bound. General minimum variance and best linear unbiased estimation. Maximum likelihood estimation, least squares, method of moments, Bayesian estimation. Extensions for complex data and parameters.

## Behörighet

För fristående kursstuderande: 180hp  samt engelska B eller motsvarande

EQ2300 Digital Signal Processing grade 4 or 5 and the permission of the examiner.
EQ2820 Matrix Algebra, accelerated program is recommended but not required.

## Litteratur

"Fundamentals of Statistical Signal Processing: Estimation Theory," Kay, Steven M. ISBN 0133457117.

## Examination

• LAB1 - Laboration, 1,5, betygsskala: P, F
• TEN1 - Tentamen, 4,5, betygsskala: A, B, C, D, E, FX, F

## Krav för slutbetyg

Attending the lectures is mandatory
Homework assignments oral examination (not required if the homeworks are correct) project assignment.

## Ges av

EECS/Intelligenta system

## Kontaktperson

Magnus Jansson (janssonm@kth.se)

## Examinator

Magnus Jansson <janssonm@kth.se>

## Övrig information

Given in P1 every odd year.

## Versionsinformation

Kursplan gäller från och med VT2019.
Examinationsinformation gäller från och med VT2019.