There is a typo in the last line of the problem 5. There is no "s" in the problem, so you can just neglect the line "when s>t".
Course will be graded with grades 1-5 based on points obtained from home assignments and lecture diary. The additional 5cr project work is graded based on a poster presentation.
Master's Programme in Mathematics and Statistics is responsible for the course.
The course belongs to the Mathematics and Applied mathematics module.
The course is available to students from other degree programmes.
Probability theory, Measure and integration theory
Learning the framework for statistical Bayesian inverse problems. Understand main computational and theoretical ideas of uncertainty quantification via the Bayes formula. Learn efficient computational methods for uncertainty quantification.
Recommended time/stage of studies for completion: 1. or 2. year
Term/teaching period when the course will be offered: varying
Bayesian approach to ill-posed inverse problems. Computational methods for exploring the posterior distribution. Theory of well-posedness and stability properties of Bayesian inversion. Practical project work in the Industrial Mathematics Laboratory.
Lectures and exercise classes, including Matlab programming in computer class
Exam and excercises, Course will be graded with grades 1-5
Exam, other methods will be described later