Next week Chris Oates and I will host the SAMSI–Lloyds–Turing Workshop on Probabilistic Numerical Methods at the Alan Turing Institute, London, housed in the British Library. The workshop is being held as part of the SAMSI Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics.
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.
This workshop has been made possible by the generous support of SAMSI, the Alan Turing Institute, and the Lloyd's Register Foundation Data-Centric Engineering Programme.