Tim Sullivan

Junior Professor in Applied Mathematics:
Risk and Uncertainty Quantification

Optimal uncertainty quantification for legacy data observations of Lipschitz functions

UQ for Legacy Data from Lipschitz Functions in M2AN

Mathematical Modelling and Numerical Analysis has just published a paper by Mike McKerns, Dominic Meyer, Florian Theil, Houman Owhadi and Michael Ortiz and myself on optimal UQ for legacy data observations of Lipschitz functions.

In this paper, we address both mathematically and numerically the challenge of giving optimal bounds on quantities of interest of the form \(\mathbb{P}_{X \sim \mu}[f(X) \geq t]\), where the probability distribution \(\mu\) of \(X\) is only partially known through some of its moments, and the forward model \(f\) is partially known through some pointwise observations and smoothness information.

T. J. Sullivan, M. McKerns, D. Meyer, F. Theil, H. Owhadi & M. Ortiz. “Optimal uncertainty quantification for legacy data observations of Lipschitz functions.” ESAIM. Mathematical Modelling and Numerical Analysis 47(6):1657–1689, 2013. doi:10.1051/m2an/2013083

Published on Friday 30 August 2013 at 18:00 UTC #publication