3 citations · 9 across the 26 of their papers we have counts for
6 papers · 1 filter
GaussED: A Probabilistic Programming Language for Sequential Experimental Design
Matthew A. Fisher, Onur Teymur, Chris. J. Oates
Sequential algorithms are popular for experimental design, enabling emulation, optimisation and inference to be efficiently performed. For most of these applications bespoke softwa…
Minimum Discrepancy Methods in Uncertainty Quantification
Chris J. Oates
The lectures were prepared for the École Thématique sur les Incertitudes en Calcul Scientifique (ETICS) in September 2021.
Black Box Probabilistic Numerics
Onur Teymur, Christopher N. Foley, Philip G. Breen +2
Probabilistic numerics casts numerical tasks, such the numerical solution of differential equations, as inference problems to be solved. One approach is to model the unknown quanti…
Bayesian Numerical Methods for Nonlinear Partial Differential Equations
Junyang Wang, Jon Cockayne, Oksana Chkrebtii +2
The numerical solution of differential equations can be formulated as an inference problem to which formal statistical approaches can be applied. However, nonlinear partial differe…
Post-Processing of MCMC
Leah F. South, Marina Riabiz, Onur Teymur +1
Markov chain Monte Carlo (MCMC) is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of…
Probabilistic Iterative Methods for Linear Systems
Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates +1
This paper presents a probabilistic perspective on iterative methods for approximating the solution of a nonsingular linear system $\mathbf{A} \math…