Jon Cockayne

According to our database, Jon Cockayne authored at least 15 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Statistical properties of BayesCG under the Krylov prior.

Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates

Numerische Mathematik, December, 2023

Theoretical Guarantees for the Statistical Finite Element Method.

Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan

SIAM/ASA J. Uncertain. Quantification, December, 2023

2022

Testing Whether a Learning Procedure is Calibrated.

Jon Cockayne, Matthew M. Graham, Chris J. Oates, Timothy John Sullivan, Onur Teymur

J. Mach. Learn. Res., 2022

Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG.

Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates

CoRR, 2022

2021

Bayesian numerical methods for nonlinear partial differential equations.

Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates

Stat. Comput., 2021

Probabilistic Gradients for Fast Calibration of Differential Equation Models.

Jonathan Cockayne, Andrew B. Duncan

SIAM/ASA J. Uncertain. Quantification, 2021

Probabilistic Iterative Methods for Linear Systems.

Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid

J. Mach. Learn. Res., 2021

A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations.

Toni Karvonen, Jon Cockayne, Filip Tronarp, Simo Särkkä

CoRR, 2021

2020

A Probabilistic Numerical Extension of the Conjugate Gradient Method.

Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates

CoRR, 2020

2019

Bayesian Probabilistic Numerical Methods.

Jon Cockayne, Chris J. Oates, Timothy John Sullivan, Mark A. Girolami

SIAM Rev., 2019

Probabilistic linear solvers: a unifying view.

Simon Bartels, Jon Cockayne, Ilse C. F. Ipsen, Philipp Hennig

Stat. Comput., 2019

2018

A Bayesian Conjugate Gradient Method.

Jon Cockayne, Chris J. Oates, Mark A. Girolami

CoRR, 2018

2017

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems.

Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami

CoRR, 2017

On the Sampling Problem for Kernel Quadrature.

François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems.

Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami

CoRR, 2016