| Bayesian probabilistic numerical methods J Cockayne, CJ Oates, TJ Sullivan, M Girolami SIAM review 61 (4), 756-789, 2019 | 215 | 2019 |
| 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 | 78 | 2022 |
| Convergence rates for a class of estimators based on Stein’s method CJ Oates, J Cockayne, FX Briol, M Girolami | 62 | 2019 |
| A Bayesian conjugate gradient method (with discussion) J Cockayne, CJ Oates, ICF Ipsen, M Girolami | 57* | 2019 |
| Probabilistic numerical methods for PDE-constrained Bayesian inverse problems J Cockayne, C Oates, T Sullivan, M Girolami AIP Conference Proceedings 1853 (1), 060001, 2017 | 57 | 2017 |
| Probabilistic meshless methods for partial differential equations and Bayesian inverse problems J Cockayne, C Oates, TJ Sullivan, M Girolami | 37 | 2016 |
| Bayesian numerical methods for nonlinear partial differential equations J Wang, J Cockayne, O Chkrebtii, TJ Sullivan, CJ Oates Statistics and Computing 31 (5), 55, 2021 | 34 | 2021 |
| 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 | 32 | 2019 |
| Probabilistic linear solvers: a unifying view S Bartels, J Cockayne, ICF Ipsen, P Hennig Statistics and Computing 29 (6), 1249-1263, 2019 | 31 | 2019 |
| 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 | 26 | 2016 |
| 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 | 23 | 2017 |
| 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 | 22 | 2016 |
| 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 | 15 | 2022 |
| Probabilistic iterative methods for linear systems J Cockayne, ICF Ipsen, CJ Oates, TW Reid Journal of machine learning research 22 (232), 1-34, 2021 | 11 | 2021 |
| Theoretical guarantees for the statistical finite element method Y Papandreou, J Cockayne, M Girolami, A Duncan SIAM/ASA Journal on Uncertainty Quantification 11 (4), 1278-1307, 2023 | 8 | 2023 |
| On the Bayesian solution of differential equations J Wang, J Cockayne, C Oates arXiv preprint arXiv:1805.07109, 2018 | 8 | 2018 |
| Bayesian probabilistic numerical methods for industrial process monitoring CJ Oates, J Cockayne, RG Aykroyd arXiv preprint arXiv:1707.06107 1707, 2017 | 8 | 2017 |
| Probabilistic gradients for fast calibration of differential equation models J Cockayne, A Duncan SIAM/ASA Journal on Uncertainty Quantification 9 (4), 1643-1672, 2021 | 7 | 2021 |
| A role for symmetry in the Bayesian solution of differential equations J Wang, J Cockayne, CJ Oates | 7 | 2020 |
| Bayesian probabilistic numerical methods (2017) J Cockayne, C Oates, T Sullivan, M Girolami arXiv preprint arXiv:1702.03673, 0 | 7 | |
Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
