Stochastic Modelling and Processing





Offered by

ICT Engineering




Upper level mathematics equivalent to A-levels. Calculus.

There is a limit of 45 participants in the course. In the event that more than 45 students select the course, we will select the 45 students based on their grades in MSE1 or other equivalent math courses.

Main purpose

The ubiquitous presence of uncertainty and noise in the engineering sciences makes it mandatory to understand and quantify random phenomena. To achieve this goal the course will provide a solid introduction to the theory of stochastic processes. Special attention is given to applications and the student will model and analyse complex stochastic situations as encountered in practice. The applications include examples from various engineering fields such as information technologies and communications, signal processing, and more.


After successfully completing the course, the student will have gained knowledge about:
  • The main working tools and concepts of stochastic modelling
  • Probability theory and distributions
  • Confidence Intervals and Hypothesis Testing
  • Inferential statistics


After successfully completing the course, the student will be able to:
  • Apply results from basic probability theory including conditional probability
  • Use probability density and distributions functions of one and two variables
  • Account for random variables and random processes
  • Calculate and estimate errors and uncertainties.


After successfully completing the course, the student will have acquired competencies in:
  • Planning experiments and state hypothesis
  • Presenting statistical results from experiments
  • Modelling experimental data with regression
  • Analysing experimental results and test hypotheses


  • Experiments and the concepts of probability
  • Calculations of probability
  • Often encountered probability density and distribution functions
  • Random variables and random processes
  • Analysis of errors in experiments
  • Design of statistical experiments
  • Creating hypotheses and confidence intervals
  • Presentation of statistical data
  • Linear and exponential regression

Teaching methods and study activities

The course is taught as an intensive 3-week course starting the first Wednesday in the final week of the exam period in January. There are 40 theoretical lessons and 20 exercise lessons, a total of 60 lessons. One lesson is 45 minutes. The total workload is expected to be around 120 hours.


Python 3.X
Montgomery, D.C. & Runger, G.C. Applied Statistics and Probability for Engineers, 4th edition Wiley (obtained from library)
Montgomery, D.C. & Runger, G.C. Applied Statistics and Probability for Engineers, 7th edition Wiley (e-book)


Grading will be done according to the 7-scale, using an internal examiner.


‚ÄčThe final exam is a 3 hour written exam and takes place at Campus Horsens. All supplementary materials and aids are allowed, e.g. using a computer as a reference work.
Communication of any sort is not allowed during the exam and will lead to
expulsion of all involved parties from the exam.

The exam must be completed in the Jupyter Notebook environment and the answers must be submitted in Wiseflow.

The re-exam may be held as an oral examination.

Grading criteria

According to the 7-point grading scale, interrnal examiner.
Mark 12:
Awarded to students who have shown excellent comprehension of the above-mentioned competences. A few minor errors and shortfalls are acceptable.
Mark 02:
Awarded to students for the just acceptable level of comprehension of the required competences.

Additional information

For more information, please contact Richard Brooks (


Richard Brooks

Valid from

8/1/2019 12:00:00 AM

Course type

6. semester
7. semester
Elective for the specialization Data Engineering


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