Whats is a Gaussian inverse problem?

IMPORTANT: This colloquium will take place at Campus Griebnitzsee, 3.06.H01!

This ‘What is …?’ lecture is in preparation for the colloquium by Carel Peeters.

Abstract: A linear Gaussian inverse problem is the task of recovering an unknown quantity from noisy, indirect observations using a linear model. The unknown is related to the observed data through a linear operator, while the measurement noise and often the unknown itself are assumed to follow Gaussian distributions. These problems provide a simple yet powerful mathematical framework for studying how to reconstruct unknown information from incomplete or noisy data. In this talk, I will introduce the basic setup of linear Gaussian inverse problems, explain the main ideas behind their formulation, and discuss how Gaussian assumptions help us understand and solve them.