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.
Uncertainty Quantification for Bayesian Inverse Problems - Efficient Methods in the Small Noise Regime
Isaac Newton Institute for Mathematical Sciences via YouTube
Overview
Syllabus
Date: 11 April 2023 - 16:00 to
Taught by
Isaac Newton Institute for Mathematical Sciences
Related Courses
-
Introduction to Uncertainty Quantification
-
From Quantum Entanglement to Future Data-Driven Engineering - Rothschild Lecture
-
Bayesian Imaging with Deep Generative Priors
-
Mathematical Aspects of Uncertainty Quantification for Interatomic Potentials
-
Admitting Uncertainty: On the Role of Probability in Finance - Rothschild Lecture
-
Variational Bayesian Approximation of Inverse Problems Using Sparse Precision Matrices
