Jon Cockayne
Jon Cockayne
Verified email at warwick.ac.uk - 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
652019
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, 2016
40*2016
Probabilistic meshless methods for partial differential equations and Bayesian inverse problems
J Cockayne, C Oates, TJ Sullivan, M Girolami
192016
On the sampling problem for kernel quadrature
FX Briol, CJ Oates, J Cockayne, WY Chen, M Girolami
arXiv preprint arXiv:1706.03369, 2017
152017
Probabilistic numerical methods for PDE-constrained Bayesian inverse problems
J Cockayne, C Oates, T Sullivan, M Girolami
AIP Conference Proceedings 1853 (1), 060001, 2017
132017
Bayesian probabilistic numerical methods (2017)
J Cockayne, C Oates, T Sullivan, M Girolami
arXiv preprint arXiv:1702.03673, 0
13
A Bayesian conjugate gradient method
J Cockayne, C Oates, I Ipsen, M Girolami
arXiv preprint arXiv:1801.05242, 2018
11*2018
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
82019
Bayesian probabilistic numerical methods for industrial process monitoring
CJ Oates, J Cockayne, RG Aykroyd
arXiv preprint arXiv:1707.06107, 2017
82017
Probabilistic linear solvers: a unifying view
S Bartels, J Cockayne, ICF Ipsen, P Hennig
Statistics and Computing 29 (6), 1249-1263, 2019
72019
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
72016
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
6
On the Bayesian solution of differential equations
J Wang, J Cockayne, C Oates
arXiv preprint arXiv:1805.07109, 2018
52018
Optimal Thinning of MCMC Output
M Riabiz, W Chen, J Cockayne, P Swietach, SA Niederer, L Mackey, ...
arXiv preprint arXiv:2005.03952, 2020
32020
Exponential stability of solutions to stochastic differential equations driven by G-LÚvy process
B Wang, H Gao
Applied Mathematics & Optimization, 1-28, 2019
32019
Optimality criteria for probabilistic numerical methods
CJ Oates, J Cockayne, D Prangle, TJ Sullivan, M Girolami
32019
Probabilistic meshless methods for Bayesian inverse problems
J Cockayne, CJ Oates, T Sullivan, M Girolami
arXiv preprint arXiv:1605.07811, 2016
32016
Comments on" Bayesian Solution Uncertainty Quantification for Differential Equations" by Chkrebtii, Campbell, Calderhead & Girolami
J Cockayne
arXiv preprint arXiv:1610.08363, 2016
22016
A Role for Symmetry in the Bayesian Solution of Differential Equations
J Wang, J Cockayne, CJ Oates
Bayesian Analysis, 2018
12018
Contributed discussion on article by Chkrebtii, Campbell, Calderhead, and Girolami
FX Briol, J Cockayne, O Teymur
Bayesian Analysis 11 (4), 1285-1293, 2016
12016
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