EQ2810 Estimation Theory, Accelerated Program Course 6.0 credits

Estimeringsteori, forskarförberedande

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.

  • Education cycle

    Second cycle
  • Main field of study

    Electrical Engineering
  • Grading scale

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

Intended learning outcomes

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.

Course main content

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.

Eligibility

For single course students: 180 credits and documented proficiency in English B or equivalent

Recommended prerequisites

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

Literature

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

Examination

  • LAB1 - Laboratory Work, 1.5, grading scale: P, F
  • TEN1 - Examination, 4.5, grading scale: A, B, C, D, E, FX, F

Requirements for final grade

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

Offered by

EECS/Intelligent Systems

Contact

Magnus Jansson (janssonm@kth.se)

Examiner

Magnus Jansson <janssonm@kth.se>

Supplementary information

Given in P1 every odd year. 

Version

Course syllabus valid from: Spring 2019.
Examination information valid from: Spring 2019.