LS00EK97 Statistical Inverse Problems (5 cr)
Description
- Bayesian interpretation of inverse problems. - Prior- and likelihood models - Posterior density models. - Inference over probability densities. - Markov chain Monte Carlo (MCMC) -algorithms. - Non-stationary inverse problems and Kalman-filters. Modelling and approximation errors.
Learning outcomes
Students will learn the basics of Bayesian inverse problems and they will learn how to apply the theory and computational methods to computational inverse problems in practice. The course develops the following generic skills: digitalization, management and development, internationality, sustainability and responsibility, critical thinking, identification and development of one's own expertise, and interaction and communication.
Additional information
Time: The course will be held only every second academic year. You can see the course implementations in Study guide, under "Course Units" or "Show past courses". This course is intended for the following student groups: - Undergraduate students in Technical Physics (MSc) - Undergraduate students in Applied Physics (MSc) - Graduate students in technology, physics and mathematics.
Description of prerequisites
Recommendations for the prior learning: Inverse Problems, Matlab programming