Welcome to Estimation theory for signal processing and communications


An estimator is a way to “guess” the value of an entity from data stochastically related to it.  Estimation theory, which may be considered as an element of “machine learning”, is highly relevant for researchers who are often facing problems of inferring parameters from data gathered from measurements or simulations.  This is in particular true for researchers within communications and signal processing.


The aim of this course is to give an overview of the field of estimation theory and the tools to derive and evaluate new estimators for specific engineering applications.  The course is centered around three main topics:

  1. Introduction to point estimators and performance bounds
  2. Connection between estimation theory and information theory
  3. Bayesian inference on graphical models


The teachers of the course are all actively developing estimators as a part of their research. Therefore, we will during the course give examples of how we use the theory in our own research work.


Background in probability theory and statistics at the level taught at the MSc at AAU. Some background in stochastic processes is useful, but not required.

Learning objectives:

The intended learning outcome is that the participants will be able to apply methods from the course to problems within their own fields of research.

Teaching methods:

The course will consist of lectures combined with exercises and work on the students own estimation problem. As a part of the exercises, the students will formulate an estimation/inference problem related to their field of research and to apply some of the methods to that problem.


Criteria for assessment:

Active participation is expected. At the end of the course, the students will present their results of the work during the course and give/receive feedback from lecturers and peers.

Key literature:

The course will be based on lecture notes writing by the teachers supplemented by selected chapters from text books by SM Kay, Scharf, van den Boos, Bishop and Cover&Thomas (TBC).

Organizer: Assoc. Prof. Troels Pedersen, Dept Electronic Systems (WCN). troels@es.aau.dk

Lecturers: Assoc Prof. Troels Pedersen (Dept. ES / WCN), Prof. Jan Østergaard (Dept. ES / SIP), Assoc, Prof. Carles Navarro Manchon, (Dept. ES / WCN), Assoc Prof. Elisabeth de Carvalho (Dept. ES / CNT)


Time: 6 November - 6 December 2018

Nov 6, (Half day) 13:00—16:00, Fredrik Bajers Vej 7, room B2-107

Nov 7, 9:00—12:00, 13:00—16:00, Niels Jernes Vej 12, room A5-006

Nov 9, 9:00—12:00, 13:00—16:00, Niels Jernes Vej 12, room A5-006

Nov 12, 9:00—12:00, 13:00—16:00, Niels Jernes Vej 12, room A5-006

Nov 13 (Half day) , 9:00—12:00, Niels Jernes Vej 12, room A5-006

Nov 20, 9:00—12:00, 13:00—16:00, Niels Jernes Vej 12, room A5-006

Nov 22 (half day), 9:00—12:00, Fredrik Bajers Vej 7, room B2-107


Exam: Dec 6 (Half day), 9:00—12:00, Fredrik Bajers Vej 7, room B2-107


Zip code: 


Number of seats: 50


Important information concerning PhD courses 
We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.