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Jon Cockayne
Jon Cockayne
The Alan Turing Institute
没有经过验证的电子邮件地址 - 首页
标题
引用次数
引用次数
年份
Bayesian probabilistic numerical methods
J Cockayne, CJ Oates, TJ Sullivan, M Girolami
SIAM review 61 (4), 756-789, 2019
1762019
Optimal thinning of MCMC output
M Riabiz, WY Chen, J Cockayne, P Swietach, SA Niederer, L Mackey, ...
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022
542022
Convergence rates for a class of estimators based on Stein’s method
CJ Oates, J Cockayne, FX Briol, M Girolami
522019
A Bayesian conjugate gradient method (with discussion)
J Cockayne, CJ Oates, ICF Ipsen, M Girolami
45*2019
Probabilistic numerical methods for PDE-constrained Bayesian inverse problems
J Cockayne, C Oates, T Sullivan, M Girolami
AIP Conference Proceedings 1853 (1), 2017
452017
Probabilistic meshless methods for partial differential equations and Bayesian inverse problems
J Cockayne, C Oates, TJ Sullivan, M Girolami
312016
Probabilistic linear solvers: a unifying view
S Bartels, J Cockayne, ICF Ipsen, P Hennig
Statistics and Computing 29, 1249-1263, 2019
292019
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, 2019
262019
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
242016
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
222017
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
222016
Bayesian numerical methods for nonlinear partial differential equations
J Wang, J Cockayne, O Chkrebtii, TJ Sullivan, CJ Oates
Statistics and Computing 31, 1-20, 2021
202021
Testing whether a learning procedure is calibrated
J Cockayne, MM Graham, CJ Oates, TJ Sullivan, O Teymur
Journal of Machine Learning Research 23 (203), 1-36, 2022
122022
On the Bayesian solution of differential equations
J Wang, J Cockayne, C Oates
arXiv preprint arXiv:1805.07109, 2018
92018
Probabilistic iterative methods for linear systems
J Cockayne, ICF Ipsen, CJ Oates, TW Reid
Journal of machine learning research 22 (232), 1-34, 2021
82021
Bayesian probabilistic numerical methods for industrial process monitoring
CJ Oates, J Cockayne, RG Aykroyd
arXiv preprint arXiv:1707.06107 1707, 2017
82017
Bayesian probabilistic numerical methods (2017)
J Cockayne, C Oates, T Sullivan, M Girolami
arXiv preprint arXiv:1702.03673, 0
8
A role for symmetry in the Bayesian solution of differential equations
J Wang, J Cockayne, CJ Oates
72020
Replication Data for: Optimal Thinning of MCMC Output, 2020b
M Riabiz, WY Chen, J Cockayne, P Swietach, SA Niederer, L Mackey, ...
URL https://doi. org/10.7910/DVN/MDKNWM. Accessed on Mar 23, 21, 2021
52021
Probabilistic gradients for fast calibration of differential equation models
J Cockayne, A Duncan
SIAM/ASA Journal on Uncertainty Quantification 9 (4), 1643-1672, 2021
52021
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