Quantifying Uncertainty: Prediction and Inverse Problems

About

Models often contain parameters which are not known exactly. We examine mathematical methods to both estimate parameters from data and to quantify the uncertainties in the outputs from the models.

Course leader

Laura Scarabosio Assistant professor Mathematics Radboud University Björn Sprungk Assistant professor Applied Mathematics TU Bergakademie Freiberg

Target group

-PhD
-Post-doc
-Professional

The course targets PhDs students, postdocs and professionals who are eager to learn more about uncertainty quantification and Bayesian inverse problems, the possible algorithms that can be used depending on the specific mathematical model as well as their theoretical foundations.

Course aim

After this course you will be able to:

- Choose the best suited algorithm to perform uncertainty quantification for a specific problem.
- Use and implement Monte Carlo, multilevel Monte Carlo and stochastic collocation.
- Formulate a Bayesian inversion problem and study its well-posedness.
- Use and implement Markov chain Monte Carlo methods.

Fee info