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