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Stochastic Modelling and Processing
Code
IT-SMP1
Version
2.0
Offered by
ICT Engineering
ECTS
5
Prerequisites
Upper level mathematics equivalent to A-levels. Calculus.
There is a limit of 40 participants in the course. In the event that more than 40 students select the course, we will select the 40 students based on their grades in DMA1 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.
Knowledge
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
Skills
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.
Competences
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
Topics
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 mode of teaching will be classroom based and will involve lectures by the teacher and going through exercises in class.
The students are also expected to work on exercises before classes.
The total work-load for the student is expected be around 130 hours.
Resources
Python 3.X
Montgomery, D.C. & Runger, G.C. Applied Statistics and Probability for Engineers, 4th edition Wiley
Montgomery, D.C. & Runger, G.C. Applied Statistics and Probability for Engineers, 7th edition Wiley (e-book)
Evaluation
Examination
Exam prerequisites:
None
Type of exam:
The exam has two parts:
• The first part is a Flowlock exam in Wiseflow.
• The second part is a Wiseflow exam without Flowlock.
The second part must be completed in the Jupyter Notebook environment and the answers must be submitted in Wiseflow.
The exam has a total duration of 4 hours.
The student will not be able to access the second part before the first part is concluded.
Each part has an equal weight in the final grade.
Internal assessment
Tools allowed: In the first part the students are allowed to use any notes, books, and/or other written/printed material and will have access to pdf files on their laptop. The student may bring their own calculator.
In the second part all supplementary materials and aids are allowed, e.g., using a computer as a reference work.
It is not allowed, however, to use AI-tools such as CoPilot, ChatGPT, Bing, etc. as per the general VIA rules.
Communication of any sort is not allowed during the exam and will lead to expulsion of all involved parties from the exam.
Re-exam:Re-exams may be oral.
Grading criteria
Grading according to the 7-point grading scale..
Additional information
Responsible
Richard Brooks (rib)
Valid from
2/1/2024 12:00:00 AM
Course type
6. semester
7. semester
Elective for the specialization Data Engineering
Electives
Web 6 og 7
Keywords
Experiments and the concepts of probability, mathematical models based on random variation