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.
From 29 August–1 September 2017, the Alan Turing Institute will host a summer school on Mathematical Aspects of Inverse Problems organised by Claudia Schillings (Mannheim) and Aretha Teckentrup (Edinburgh and Alan Turing Institute), two of my former colleagues at the University of Warwick. The invited lecturers are:
- Masoumeh Dashti (Sussex): Bayesian Approach to Inverse Problems
- Michela Ottobre (Maxwell Institute, Edinburgh): Hamiltonian Monte Carlo
- Carola-Bibiane Schönlieb and Clarice Poon (Cambridge): A Comparison of Variational Methods and Deep Neural Networks for Inverse Problems
- Alison Fowler (Reading): The Value of Observations for Numerical Weather Prediction