Content, progress and pedagogy of the
Students who complete the module:
- Have a basic understanding of probability, uncertainty,
stochastic processes and independent and conditional
- Know basic probability and statistical models for
- Have knowledge of discrete and continuous probability
distributions and their application.
- Know of the basic principles of statistical analysis, including
- Have knowledge about statistical inferens and hypothesis
- 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
- Are able to use probability distributions to describe
- 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
- Can assess the applicability of probability theory in a given
- Are able to use correct professional terminology.
- Are able to acquire additional knowledge in the
Type of instruction
Lectures, etc. supplemented with project work, workshops,
Extent and expected workload
The module is 5 ECTS which is corresponding to 150 hours of
|Name of exam||Applied statistics and Probability Theory|
|Type of exam|
Written exam based on a case.
|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|