Stochastic Processes


Recommended prerequisite for participation in the module

Solid knowledge in probability, statistics, linear algebra, Fourier theory, and programming

Content, progress and pedagogy of the module

Learning objectives


  • Have knowledge about the theoretical framework in which stochastic processes are defined
  • Be able to understand the properties of the stochastic processes introduced in the course, such as white-sense stationary (WSS) processes, Auto Regressive Moving Average (ARMA) processes, Markov models, and Poisson point processes
  • Be able to understand how WSS process are transformed by linear-invariant systems
  • Be able to understand the theoretical context around the introduced estimation and detection methods ((non-parametric and parametric) spectral estimation, Linear Minimum Mean Square Error (LMMSE) estimation, Wiener filter, Kalman filter, detection of signals, ARMA estimation, etc.)


  • Be able to apply the stochastic processes taught in the course to model real random mechanisms occurring in engineering problems.
  • Be able to simulate stochastic processes using a standard programming language.
  • Be able to apply the taught estimation and detection methods to solve engineering problems dealing with random mechanisms.
  • Be able to evaluate the performance of the introduced estimation and detection methods


  • Have the appropriate “engineering” intuition of the basics concepts and results related to stochastic processes that allow – for a particular engineering problem involving randomness – to design an appropriate model, derive solutions, assess the performance of these solutions, and possibly modify the model, and all subsequent analysis steps, if necessary.

Type of instruction

The programme is based on a combination of academic, problem-oriented and interdisciplinary approaches and organised based on the following work and evaluation methods that combine skills and reflection:

  • Lectures
  • Classroom instruction
  • Project work
  • Workshops
  • Exercises (individually and in groups)
  • Teacher feedback
  • Reflection
  • Portfolio work

Extent and expected workload

Since it is a 5 ECTS course module, the work load is expected to be 150 hours for the student



Name of examStochastic Processes
Type of exam
Written or oral exam
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleStokastiske processer
Module codeN-IRS-K1-2
Module typeCourse
Duration1 semester
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Esbjerg
Responsible for the module


Education ownerMaster of Science (MSc) in Engineering (Intelligent Reliable Systems)
Study BoardStudy Board of Build, Energy, Electronics and Mechanics in Esbjerg
DepartmentDepartment of Energy
FacultyThe Faculty of Engineering and Science