Toggle Main Menu Toggle Search

Open Access padlockePrints

Browsing publications by Professor Chris Oates.

Newcastle AuthorsTitleYearFull text
Takuo Matsubara
Professor Chris Oates
Generalized Bayesian Inference for Discrete Intractable Likelihood2024
Professor Emilio Porcu
Professor Chris Oates
The Matérn Model: A Journey Through Statistics, Numerical Analysis and Machine Learning2024
Professor Chris Oates
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators2023
Matthew Fisher
Professor Chris Oates
Gradient-Free Kernel Stein Discrepancy2023
Matthew Fisher
Professor Chris Oates
Gradient-Free Kernel Stein Discrepancy2023
Dr Toni Karvonen
Professor Chris Oates
Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed2023
Professor Chris Oates
Meta-learning Control Variates: Variance Reduction with Limited Data2023
Professor Chris Oates
Regularized Zero-Variance Control Variates2023
Professor Chris Oates
Review of "Probabilistic Numerics" by Hennig, Osborne and Kersting2023
Professor Emilio Porcu
Professor Chris Oates
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres2023
Professor Chris Oates
Statistical properties of BayesCG under the Krylov prior2023
Professor Chris Oates
Statistical properties of BayesCG under the Krylov prior2023
Congye Wang
Dr Heishiro Kanagawa
Professor Chris Oates
Stein Π-Importance Sampling2023
Professor Chris Oates
Stein’s Method Meets Computational Statistics: A Review of Some Recent Developments2023
Professor Chris Oates
Professor Emilio Porcu
A Riemann–Stein kernel method2022
Professor Chris Oates
Dr Liam Fleming
A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel2022
Professor Chris Oates
Minimum Kernel Discrepancy Estimators2022
Professor Chris Oates
Optimal thinning of MCMC output2022
Professor Chris Oates
Parameter Space Reduction for Four-chamber Electromechanics Simulations Using Gaussian Processes Emulators2022
Professor Chris Oates
Postprocessing of MCMC2022
Takuo Matsubara
Professor Chris Oates
Robust generalised Bayesian inference for intractable likelihoods2022
Professor Chris Oates
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization2022
Professor Chris Oates
Semi-Exact Control Functionals From Sard's Method2022
Dr Matt Graham
Professor Chris Oates
Testing Whether a Learning Procedure is Calibrated2022
Dr Liam Fleming
Professor Peter Gosling
Professor Chris Oates
A Data-Centric Approach to Generative Modelling for 3D-Printed Steel2021
Dr Junyang Wang
Professor Chris Oates
Bayesian numerical methods for nonlinear partial differential equations2021
Dr Onur Teymur
Professor Chris Oates
Black Box Probabilistic Numerics2021
Professor Chris Oates
Professor Kevin Wilson
Causal Graphical Models for Systems-Level Engineering Assessment2021
Professor Chris Oates
Improved Calibration of Numerical Integration Error in Sigma-Point Filters2021
Professor Chris Oates
Integration In Reproducing Kernel Hilbert Spaces Of Gaussian Kernels2021
Matthew Fisher
Dr Matt Graham
Dr Dennis Prangle
Professor Chris Oates
Measure Transport with Kernel Stein Discrepancy2021
Matthew Fisher
Dr Matt Graham
Dr Dennis Prangle
Professor Chris Oates
Measure Transport with Kernel Stein Discrepancy2021
Dr Onur Teymur
Professor Chris Oates
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy2021
Dr Onur Teymur
Professor Chris Oates
Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy2021
Professor Chris Oates
Probabilistic Iterative Methods for Linear Systems2021
Takuo Matsubara
Professor Chris Oates
The ridgelet prior: A covariance function approach to prior specification for Bayesian neural networks2021
Matthew Fisher
Professor Chris Oates
A Locally Adaptive Bayesian Cubature Method2020
Professor Chris Oates
A Role for Symmetry in the Bayesian Solution of Differential Equations2020
Professor Chris Oates
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions2020
Professor Chris Oates
Dr Dennis Prangle
Optimality Criteria for Probabilistic Numerical Methods2020
Professor Chris Oates
A Bayesian Conjugate Gradient Method2019
Professor Chris Oates
A modern retrospective on probabilistic numerics2019
Professor Chris Oates
Bayesian Probabilistic Numerical Methods2019
Professor Chris Oates
Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment2019
Professor Chris Oates
Causal learning via manifold regularization2019
Professor Chris Oates
Convergence rates for a class of estimators based on Stein’s method2019
Professor Chris Oates
Editorial: special edition on probabilistic numerics2019
Professor Chris Oates
Optimal Monte Carlo integration on closed manifolds2019
Professor Chris Oates
Probabilistic Integration: A Role in Statistical Computation?2019
Professor Chris Oates
Rejoinder: Probabilistic Integration: A Role in Statistical Computation?2019
Professor Chris Oates
Stein Point Markov Chain Monte Carlo2019
Professor Chris Oates
Symmetry Exploits for Bayesian Cubature Methods2019
Professor Chris Oates
A Bayes-Sard Cubature Method2018
Professor Chris Oates
Graphical Models in Molecular Systems Biology2018
Professor Chris Oates
On the Bayesian Solution of Differential Equations2018
Professor Chris Oates
Stein Points2018
Professor Chris Oates
Control functionals for Monte Carlo integration2017
Professor Chris Oates
Investigation of the Widely Applicable Bayesian Information Criteria2017
Professor Chris Oates
On the Sampling Problem for Kernel Quadrature2017
Professor Chris Oates
Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models2017
Professor Chris Oates
Repair of Partly Misspecified Causal Diagrams2017
Professor Chris Oates
Control Functionals for Quasi-Monte Carlo Integration2016
Professor Chris Oates
Estimating Causal Structure Using Conditional DAG Models2016
Professor Chris Oates
Exact Estimation of Multiple Directed Acyclic Graphs2016
Professor Chris Oates
Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods2016
Professor Chris Oates
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems2016
Professor Chris Oates
RNA editing generates sequence diversity within cell populations2016
Professor Chris Oates
The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation2016
Professor Chris Oates
Accelerated Non-parametrics for Cascades of Poisson Processes2015
Professor Chris Oates
Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer2015
Professor Chris Oates
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees2015
Professor Chris Oates
Towards a Multisubject Analysis of Neural Connectivity2015
Professor Chris Oates
A stochastic model dissects cell states in biological transition processes2014
Professor Chris Oates
Causal network inference using biochemical kinetics2014
Professor Chris Oates
Joint Estimation of Multiple Related Biological Networks2014
Professor Chris Oates
Sunil Mukherjee
Joint Structure Learning of Multiple Non-Exchangeable Networks2014
Professor Chris Oates
Quantifying the Multi-Scale Performance of Network Inference Algorithms2014
Professor Chris Oates
Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines2014