Entry requirements

Applied Statistics or Probability Theory and Inference Theory.

Learning outcomes

In order to pass the course the student should be able to

• use Bayesian methodology for estimating failure intensities and risks and evaluate the results;
• formulate models where the Poisson process, in time or space, is used for modelling of rare events and related risks;
• use basic methodology for statistical analysis of extreme values;
• choose suitable statistical methodology for probabilistic risk analysis within applications from technology, the natural or social sciences.

Content

Conditional distributions. Bayesian methods for estimation of failure intensities and risks. The Poisson process and Poisson regression. Extreme-value analysis.

Instruction

Lectures, computersessions. Guest lecture. Case studies where the course content is applied in problems arising in technology, the natural or social sciences.

Assessment

Written examination at the end of the course (4 credits) combined with assignments during the course (1 credit)