Content, progress and pedagogy of the
module
Students who complete the module:
Learning objectives
Knowledge
- Have a basic understanding of probability, uncertainty,
stochastic processes and independent and conditional
probabilities.
- Know basic probability and statistical models for
uncertainties.
- Have knowledge of discrete and continuous probability
distributions and their application.
- Know of the basic principles of statistical analysis, including
data collection.
- Have knowledge about statistical inferens and hypothesis
testing.
- Know the principles of Markov chains and Monte Carlo methods to
simulate probability distributions.
- Understands the limitations of models/tools within risk/safety,
especially in relation to the input data’s validity and
credibility.
Skills
- Are able to use probability distributions to describe
stochastic processes.
- Can estimate statistical parameters from a dataset.
- Can compute confidence intervals.
- Can account for the theory behind applied models.
- Are able to use relevant statistical software to
approximate a posteriori probability
distributions.
Competences
- Can assess the applicability of probability theory in a given
situation.
- Are able to use correct professional terminology.
- Are able to acquire additional knowledge in the
field.
Type of instruction
Lectures, etc. supplemented with project work, workshops,
presentation seminars.
Extent and expected workload
The module is 5 ECTS which is corresponding to 150 hours of
study.
Exam
Exams
Name of exam | Applied statistics and Probability Theory |
Type of exam | Written exam
Written exam based on a case. |
ECTS | 5 |
Assessment | 7-point grading scale |
Type of grading | Internal examination |
Criteria of assessment | The criteria of assessment are stated in the Examination
Policies and Procedures |