Jon Cockayne
Jon Cockayne
The Alan Turing Institute
No verified email - Homepage
Title
Cited by
Cited by
Year
Bayesian probabilistic numerical methods
J Cockayne, CJ Oates, TJ Sullivan, M Girolami
SIAM Review 61 (4), 756-789, 2019
962019
Convergence rates for a class of estimators based on Stein’s identity
CJ Oates, J Cockayne, FX Briol, M Girolami
arXiv preprint arXiv:1603.03220 6, 2016
42*2016
Probabilistic meshless methods for partial differential equations and Bayesian inverse problems
J Cockayne, C Oates, TJ Sullivan, M Girolami
282016
A Bayesian conjugate gradient method (with discussion)
J Cockayne, CJ Oates, ICF Ipsen, M Girolami
Bayesian Analysis 14 (3), 937-1012, 2019
22*2019
On the sampling problem for kernel quadrature
FX Briol, CJ Oates, J Cockayne, WY Chen, M Girolami
International Conference on Machine Learning, 586-595, 2017
172017
Probabilistic numerical methods for PDE-constrained Bayesian inverse problems
J Cockayne, C Oates, T Sullivan, M Girolami
AIP Conference Proceedings 1853 (1), 060001, 2017
172017
Probabilistic linear solvers: a unifying view
S Bartels, J Cockayne, ICF Ipsen, P Hennig
Statistics and Computing 29 (6), 1249-1263, 2019
162019
Bayesian probabilistic numerical methods in time-dependent state estimation for industrial hydrocyclone equipment
CJ Oates, J Cockayne, RG Aykroyd, M Girolami
Journal of the American Statistical Association 114 (528), 1518-1531, 2019
152019
Probabilistic numerical methods for partial differential equations and Bayesian inverse problems
J Cockayne, C Oates, T Sullivan, M Girolami
arXiv preprint arXiv:1605.07811, 2016
122016
Bayesian probabilistic numerical methods for industrial process monitoring
CJ Oates, J Cockayne, RG Aykroyd
arXiv preprint arXiv:1707.06107, 2017
82017
On the Bayesian solution of differential equations
J Wang, J Cockayne, C Oates
arXiv preprint arXiv:1805.07109, 2018
72018
Optimal thinning of mcmc output
M Riabiz, W Chen, J Cockayne, P Swietach, SA Niederer, L Mackey, ...
arXiv preprint arXiv:2005.03952, 2020
62020
Probabilistic meshless methods for Bayesian inverse problems
J Cockayne, CJ Oates, T Sullivan, M Girolami
arXiv preprint arXiv:1605.07811, 2016
62016
Bayesian probabilistic numerical methods (2017)
J Cockayne, C Oates, T Sullivan, M Girolami
arXiv preprint arXiv:1702.03673, 0
6
A role for symmetry in the Bayesian solution of differential equations
J Wang, J Cockayne, CJ Oates
Bayesian Analysis 15 (4), 1057-1085, 2020
52020
5. Optimality criteria for probabilistic numerical methods
CJ Oates, J Cockayne, D Prangle, TJ Sullivan, M Girolami
Multivariate Algorithms and Information-Based Complexity, 65-88, 2020
32020
Comments on" Bayesian Solution Uncertainty Quantification for Differential Equations" by Chkrebtii, Campbell, Calderhead & Girolami
J Cockayne
arXiv preprint arXiv:1610.08363, 2016
22016
Probabilistic meshless methods for partial differential equations and Bayesian inverse problems (2016)
J Cockayne, C Oates, TJ Sullivan, M Girolami
arXiv preprint arXiv:1605.07811, 0
2
Testing whether a Learning Procedure is Calibrated
J Cockayne, MM Graham, CJ Oates, TJ Sullivan
arXiv preprint arXiv:2012.12670, 2020
12020
Probabilistic Gradients for Fast Calibration of Differential Equation Models
J Cockayne, AB Duncan
arXiv preprint arXiv:2009.04239, 2020
12020
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