SAMSI-Lloyds-Turing Workshop on Probabilistic Numerical Methods

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.

The Prob Num 2018 Workshop will be held in London, UK, from 11 to 13 April 2018.

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 about probabilistic numerics, visit Prob-Num.org.

The workshop is being held as part of the SAMSI Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics. It is organised by Chris Oates and Tim Sullivan, who co-lead the SAMSI working group on Probabilistic Numerics.