Uncertainty Quantification for Bayesian Inverse Problems - Efficient Methods in the Small Noise Regime

Explore uncertainty quantification for Bayesian inverse problems in this Kirk Lecture delivered by Professor Claudia Schillings from Freie Universität Berlin. Delve into efficient methods for the small noise regime as part of the programme on the mathematical and statistical foundation of future data-driven engineering. Gain insights into advanced mathematical concepts and their applications in engineering during this 57-minute talk presented at the Isaac Newton Institute for Mathematical Sciences.