In this talk, we will show how methods for bounding approximation errors in kernel based reconstruction methods can be employed to analyze errors in Bayesian inverse problems.
We will especially focus on reconstruction methods which are capable of exploiting structural information on the function to be approximated.
Moreover, we will present an application towards inverse problems with partial differential equations motivated by fluid dynamics.
This is based on collaborations with several people who will be given credit during the talk.