Parameter Estimation Applied to Medical and Biological Sciences


Course Objective

The course treats the theory of parameter estimation problems in
general, but the theory is illustrated extensively by examples from
medical and biological sciences and brain imaging (fMRI and MEG/EEG) in
particular. Linear and non-linear regression analysis is treated, as
well as confidence intervals and significance testing. The goal of the
course is to provide insight into the theory of parameter estimation and
to develop a critical attitude towards its application and
interpretation in order to avoid inconsistent and improper use of the

Course Content

Linear-non linear parameter models, basic matrix-vector algebra,maximum
likelihood principle, correlated-uncorrelated noise, OLS, GLS, data
outliers, nuisance parameters, linear (time invariant) filters,
projection filters t-test, F-test, confidence intervals, multiple
testing, fMRI data model, missing data, MEG/EEG source localisation.
These topics are treated in the form of a series of lectures alternated
with exercises.

Extra topics: L1 en L2 norms.

Teaching Methods

Lecture and optional (MatLab) exercises.

Method of Assessment

Written exam plus bonus point for critical review of scientific paper.

Entry Requirements

In order to participate in this course, students are required to have
successfully completed Image Processing for MNS (X_422612) and Medical
Imaging for MNS (XM_0063).


A syllabus and slides will be provided by the lecturer.

Target Audience


Recommended background knowledge

Basic Matlab experience is recommended to enable completion of optional
Matlab exercises.

General Information

Course Code X_432631
Credits 6 EC
Period P4
Course Level 500
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. I.H.M. van Stokkum
Examiner dr. J.C. de Munck
Teaching Staff dr. J.C. de Munck
dr. I.H.M. van Stokkum

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Practical
Target audiences

This course is also available as: