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2020 – today
- 2024
[i13]Marvin Pförtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig:
Computation-Aware Kalman Filtering and Smoothing. CoRR abs/2405.08971 (2024)- 2023
- [j9]Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan:
Theoretical Guarantees for the Statistical Finite Element Method. SIAM/ASA J. Uncertain. Quantification 11(4): 1278-1307 (2023) - [j8]Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne
, Chris J. Oates:
Statistical properties of BayesCG under the Krylov prior. Numerische Mathematik 155(3-4): 239-288 (2023) - [j7]Toni Karvonen, Jon Cockayne, Filip Tronarp, Simo Särkkä:
A probabilistic Taylor expansion with Gaussian processes. Trans. Mach. Learn. Res. 2023 (2023) - 2022
- [j6]Jon Cockayne, Matthew M. Graham, Chris J. Oates, Timothy John Sullivan, Onur Teymur:
Testing Whether a Learning Procedure is Calibrated. J. Mach. Learn. Res. 23: 203:1-203:36 (2022) - [i12]Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates:
Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG. CoRR abs/2208.03885 (2022) - 2021
- [j5]Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid:
Probabilistic Iterative Methods for Linear Systems. J. Mach. Learn. Res. 22: 232:1-232:34 (2021) - [j4]Jonathan Cockayne, Andrew B. Duncan:
Probabilistic Gradients for Fast Calibration of Differential Equation Models. SIAM/ASA J. Uncertain. Quantification 9(4): 1643-1672 (2021) - [j3]Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates:
Bayesian numerical methods for nonlinear partial differential equations. Stat. Comput. 31(5): 55 (2021) - [i11]Toni Karvonen, Jon Cockayne, Filip Tronarp, Simo Särkkä:
A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations. CoRR abs/2102.00877 (2021) - [i10]Junyang Wang, Jon Cockayne, Oksana A. Chkrebtii, Timothy John Sullivan, Chris J. Oates:
Bayesian Numerical Methods for Nonlinear Partial Differential Equations. CoRR abs/2104.12587 (2021) - [i9]Yanni Papandreou, Jon Cockayne, Mark Girolami, Andrew B. Duncan:
Theoretical Guarantees for the Statistical Finite Element Method. CoRR abs/2111.07691 (2021) - 2020
- [i8]Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne, Chris J. Oates:
A Probabilistic Numerical Extension of the Conjugate Gradient Method. CoRR abs/2008.03225 (2020) - [i7]Jon Cockayne, Andrew B. Duncan:
Probabilistic Gradients for Fast Calibration of Differential Equation Models. CoRR abs/2009.04239 (2020) - [i6]Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, Tim W. Reid:
Probabilistic Iterative Methods for Linear Systems. CoRR abs/2012.12615 (2020)
2010 – 2019
- 2019
- [j2]Simon Bartels, Jon Cockayne, Ilse C. F. Ipsen, Philipp Hennig:
Probabilistic linear solvers: a unifying view. Stat. Comput. 29(6): 1249-1263 (2019) - [j1]Jon Cockayne, Chris J. Oates, Timothy John Sullivan, Mark A. Girolami:
Bayesian Probabilistic Numerical Methods. SIAM Rev. 61(4): 756-789 (2019) - 2018
- [i5]Jon Cockayne, Chris J. Oates, Mark A. Girolami:
A Bayesian Conjugate Gradient Method. CoRR abs/1801.05242 (2018) - 2017
- [c1]François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami:
On the Sampling Problem for Kernel Quadrature. ICML 2017: 586-595 - [i4]Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami:
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. CoRR abs/1701.04006 (2017) - [i3]Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami:
Bayesian Probabilistic Numerical Methods. CoRR abs/1702.03673 (2017) - [i2]François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark A. Girolami:
On the Sampling Problem for Kernel Quadrature. CoRR abs/1706.03369 (2017) - 2016
- [i1]Jon Cockayne, Chris J. Oates, Tim Sullivan, Mark A. Girolami:
Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. CoRR abs/1605.07811 (2016)
Coauthor Index
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