About the event
Day 1: 10:00 - 17:00
Day 2: 9:00 - 17:00
Day 3: 9:00 - 16:00
There are a limited number of places which will be allocated to applicants whose expertise is most closely aligned with the workshop content.
Further details can be found on the main event website.
This workshop is part of the SAMSI Programme on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics. In collaboration with SAMSI and Lloyd's Register Foundation.
The accuracy and robustness of numerical predictions that are based on mathematical models depend critically upon the construction of accurate discrete approximations to key quantities of interest. The exact error due to approximation will be unknown to the analyst, but worst-case upper bounds can often be obtained.
This workshop aims, instead, to further the development of Probabilistic Numerical Methods, which provide the analyst with a richer, probabilistic quantification of the numerical error in their output, thus providing better tools for reliable statistical inference Some topics that will be discussed:
- Reference priors for the probabilistic solution of differential equations.
- Heavy-tailed stable distributions for robust uncertainty quantification.
- Statistical estimation with multi-resolution operator decompositions.
- Probabilistic numerical methods as Bayesian inversion methods.
To find out more: Prob-Num.org. 