Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems. - Jon Cockayne; Chris J. Oates; Tim Sullivan 001; Mark A. Girolami (CoRR, 2016)

On the Sampling Problem for Kernel Quadrature. - François-Xavier Briol; Chris J. Oates; Jon Cockayne; Wilson Ye Chen; Mark A. Girolami (ICML, 2017)

Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. - Jon Cockayne; Chris J. Oates; Tim Sullivan 001; Mark A. Girolami (CoRR, 2017)

A Bayesian Conjugate Gradient Method. - Jon Cockayne; Chris J. Oates; Mark A. Girolami (CoRR, 2018)

Probabilistic linear solvers: a unifying view. - Simon Bartels; Jon Cockayne; Ilse C. F. Ipsen; Philipp Hennig (Stat. Comput., 2019)

Bayesian Probabilistic Numerical Methods. - Jon Cockayne; Chris J. Oates; Timothy John Sullivan; Mark A. Girolami (SIAM Rev., 2019)

A Probabilistic Numerical Extension of the Conjugate Gradient Method. - Tim W. Reid; Ilse C. F. Ipsen; Jon Cockayne; Chris J. Oates (CoRR, 2020)

A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations. - Toni Karvonen; Jon Cockayne; Filip Tronarp; Simo Särkkä (CoRR, 2021)

Probabilistic Iterative Methods for Linear Systems. - Jon Cockayne; Ilse C. F. Ipsen; Chris J. Oates; Tim W. Reid (J. Mach. Learn. Res., 2021)

Probabilistic Gradients for Fast Calibration of Differential Equation Models. - Jonathan Cockayne; Andrew B. Duncan (SIAM/ASA J. Uncertain. Quantification, 2021)

Bayesian numerical methods for nonlinear partial differential equations. - Junyang Wang; Jon Cockayne; Oksana A. Chkrebtii; Timothy John Sullivan; Chris J. Oates (Stat. Comput., 2021)

Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG. - Tim W. Reid; Ilse C. F. Ipsen; Jon Cockayne; Chris J. Oates (CoRR, 2022)

Testing Whether a Learning Procedure is Calibrated. - Jon Cockayne; Matthew M. Graham; Chris J. Oates; Timothy John Sullivan; Onur Teymur (J. Mach. Learn. Res., 2022)

A probabilistic Taylor expansion with Gaussian processes. - Toni Karvonen; Jon Cockayne; Filip Tronarp; Simo Särkkä (Trans. Mach. Learn. Res., 2023)

Theoretical Guarantees for the Statistical Finite Element Method. - Yanni Papandreou; Jon Cockayne; Mark Girolami; Andrew B. Duncan (SIAM/ASA J. Uncertain. Quantification, 2023)

Statistical properties of BayesCG under the Krylov prior. - Tim W. Reid; Ilse C. F. Ipsen; Jon Cockayne; Chris J. Oates (Numerische Mathematik, 2023)

Calibrated Computation-Aware Gaussian Processes. - Disha Hegde; Mohamed Adil; Jon Cockayne (AISTATS, 2025)

Computation-Aware Kalman Filtering and Smoothing. - Marvin Pförtner; Jonathan Wenger; Jon Cockayne; Philipp Hennig (AISTATS, 2025)

Learning to Solve Related Linear Systems. - Disha Hegde; Jon Cockayne (CoRR, 2025)

Randomised Postiterations for Calibrated BayesCG. - Niall Vyas; Disha Hegde; Jon Cockayne (CoRR, 2025)