Statistical Inverse Problems

What it is about

You learn fundamental methods for the statistical treatment of inverse problems. The course covers linear inverse problems with random noise, regularization, convergence rates, adaptive procedures and model selection; at the end you can compare the results and apply them to concrete examples.

What you will be able to do

• Explain and compare results at the interface of statistics and inverse problems
• Apply theoretical results to explicit examples
• Understand important proof techniques
• Transfer techniques to related, untreated problems

What the module consists of

• Lectureweekly lectures convey the theoretical content
• Homework sheetsbiweekly distributed tasks for independent deepening

Teaching method

• Lecture (blackboard/tablet)for conveying the theory
• Independent work on homework sheetsfor applying and practicing the methods at home