Workshop on Probabilistic Numerical Methods - Panel Discussion

This course aims to teach Probabilistic Numerical Methods for providing a richer, probabilistic quantification of numerical errors in output, enhancing tools for reliable statistical inference. The course covers topics such as reference priors for solving differential equations, heavy-tailed stable distributions for uncertainty quantification, statistical estimation with multi-resolution operator decompositions, and probabilistic numerical methods as Bayesian inversion methods. The teaching method includes a panel discussion. The intended audience for this course is analysts and professionals seeking to improve the accuracy and robustness of numerical predictions in mathematical models.