Speaker |
Francois-Xavier Briol |
|---|---|
Affiliation |
University of Warwick |
Date |
Friday, 11 March 2016 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts G08 Sir David Davies LT (TBC) |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Probabilistic numerical methods aim to model numerical error as a source of epistemic uncertainty that is subject to probabilistic analysis and reasoning, enabling the principled propagation of numerical uncertainty through a computational pipeline. This talk will present probabilistic numerical integrators based on Markov chain and Quasi Monte Carlo and prove asymptotic results on the coverage of the associated probability models for numerical integration error. The performance of probabilistic integrators is guaranteed to be no worse than non-probabilistic integrators and is, in many cases, asymptotically superior. These probabilistic integrators therefore enjoy the "best of both worlds", leveraging the sampling efficiency of advanced Monte Carlo methods whilst being equipped with valid probabilistic models for uncertainty quantification. Several applications and illustrations will be provided, including examples from computer vision and system modelling using non-linear differential equations. |
Biography |