Chris Oates

Wednesday - 26 September 2018

Time: 4.00pm

Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG

Bayesian Probabilistic Numerical Methods

The scale and complexity of modern scientific computer codes typically precludes a detailed analysis of how the code is numerically implemented. For example, multi-scale and multi-physics models of the human heart call on diverse numerical sub-routines to integrate differential equations, perform interpolation and optimise over some parameters of the model. As such, the computer output is acknowledged to be inexact and some alternative form of uncertainty quantification is needed for the output to be properly interpreted. This talk will provide an introduction to Bayesian probabilistic numerical methods, which aim to provide probabilistic uncertainty quantification for computer code output. These methods are composed of "modules" and recent work on a novel module for the iterative solution of large linear systems will be presented in detail.