Professor Chris Oates
| Scalable Monte Carlo for Bayesian Learning | 2025 |
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Takuo Matsubara
Professor Chris Oates
| Generalized Bayesian Inference for Discrete Intractable Likelihood | 2024 |
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Professor Chris Oates
| Online Semiparametric Regression via Sequential Monte Carlo | 2024 |
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Professor Chris Oates
| Probabilistic Richardson Extrapolation | 2024 |
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Professor Emilio Porcu
Professor Chris Oates
| The Matérn Model: A Journey Through Statistics, Numerical Analysis and Machine Learning | 2024 |
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Professor Chris Oates
| Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators | 2023 |
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Matthew Fisher
Professor Chris Oates
| Gradient-Free Kernel Stein Discrepancy | 2023 |
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Matthew Fisher
Professor Chris Oates
| Gradient-Free Kernel Stein Discrepancy | 2023 |
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Dr Toni Karvonen
Professor Chris Oates
| Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed | 2023 |
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Professor Chris Oates
| Meta-learning Control Variates: Variance Reduction with Limited Data | 2023 |
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Professor Chris Oates
| Regularized Zero-Variance Control Variates | 2023 |
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Professor Chris Oates
| Review of "Probabilistic Numerics" by Hennig, Osborne and Kersting | 2023 |
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Professor Emilio Porcu
Professor Chris Oates
| Sobolev Spaces, Kernels and Discrepancies over Hyperspheres | 2023 |
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Professor Chris Oates
| Statistical properties of BayesCG under the Krylov prior | 2023 |
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Professor Chris Oates
| Statistical properties of BayesCG under the Krylov prior | 2023 |
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Congye Wang
Dr Heishiro Kanagawa
Professor Chris Oates
| Stein Π-Importance Sampling | 2023 |
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Professor Chris Oates
| Stein’s Method Meets Computational Statistics: A Review of Some Recent Developments | 2023 |
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Professor Chris Oates
Professor Emilio Porcu
| A Riemann–Stein kernel method | 2022 |
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Professor Chris Oates
Dr Liam Fleming
| A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel | 2022 |
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Professor Chris Oates
| Minimum Kernel Discrepancy Estimators | 2022 |
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Professor Chris Oates
| Optimal thinning of MCMC output | 2022 |
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Professor Chris Oates
| Parameter Space Reduction for Four-chamber Electromechanics Simulations Using Gaussian Processes Emulators | 2022 |
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Professor Chris Oates
| Postprocessing of MCMC | 2022 |
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Takuo Matsubara
Professor Chris Oates
| Robust generalised Bayesian inference for intractable likelihoods | 2022 |
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Professor Chris Oates
| Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization | 2022 |
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Professor Chris Oates
| Semi-Exact Control Functionals From Sard's Method | 2022 |
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Dr Matt Graham
Professor Chris Oates
| Testing Whether a Learning Procedure is Calibrated | 2022 |
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Dr Liam Fleming
Professor Peter Gosling
Professor Chris Oates
| A Data-Centric Approach to Generative Modelling for 3D-Printed Steel | 2021 |
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Dr Junyang Wang
Professor Chris Oates
| Bayesian numerical methods for nonlinear partial differential equations | 2021 |
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Dr Onur Teymur
Professor Chris Oates
| Black Box Probabilistic Numerics | 2021 |
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Professor Chris Oates
Professor Kevin Wilson
| Causal Graphical Models for Systems-Level Engineering Assessment | 2021 |
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Professor Chris Oates
| Improved Calibration of Numerical Integration Error in Sigma-Point Filters | 2021 |
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Professor Chris Oates
| Integration In Reproducing Kernel Hilbert Spaces Of Gaussian Kernels | 2021 |
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Matthew Fisher
Dr Matt Graham
Dr Dennis Prangle
Professor Chris Oates
| Measure Transport with Kernel Stein Discrepancy | 2021 |
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Matthew Fisher
Dr Matt Graham
Dr Dennis Prangle
Professor Chris Oates
| Measure Transport with Kernel Stein Discrepancy | 2021 |
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Dr Onur Teymur
Professor Chris Oates
| Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy | 2021 |
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Dr Onur Teymur
Professor Chris Oates
| Optimal Quantisation of Probability Measures Using Maximum Mean Discrepancy | 2021 |
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Professor Chris Oates
| Probabilistic Iterative Methods for Linear Systems | 2021 |
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Takuo Matsubara
Professor Chris Oates
| The ridgelet prior: A covariance function approach to prior specification for Bayesian neural networks | 2021 |
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Matthew Fisher
Professor Chris Oates
| A Locally Adaptive Bayesian Cubature Method | 2020 |
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Professor Chris Oates
| A Role for Symmetry in the Bayesian Solution of Differential Equations | 2020 |
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Professor Chris Oates
| Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions | 2020 |
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Professor Chris Oates
Dr Dennis Prangle
| Optimality Criteria for Probabilistic Numerical Methods | 2020 |
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Professor Chris Oates
| A Bayesian Conjugate Gradient Method | 2019 |
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Professor Chris Oates
| A modern retrospective on probabilistic numerics | 2019 |
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Professor Chris Oates
| Bayesian Probabilistic Numerical Methods | 2019 |
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Professor Chris Oates
| Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment | 2019 |
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Professor Chris Oates
| Causal learning via manifold regularization | 2019 |
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Professor Chris Oates
| Convergence rates for a class of estimators based on Stein’s method | 2019 |
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Professor Chris Oates
| Editorial: special edition on probabilistic numerics | 2019 |
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Professor Chris Oates
| Optimal Monte Carlo integration on closed manifolds | 2019 |
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Professor Chris Oates
| Probabilistic Integration: A Role in Statistical Computation? | 2019 |
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Professor Chris Oates
| Rejoinder: Probabilistic Integration: A Role in Statistical Computation? | 2019 |
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Professor Chris Oates
| Stein Point Markov Chain Monte Carlo | 2019 |
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Professor Chris Oates
| Symmetry Exploits for Bayesian Cubature Methods | 2019 |
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Professor Chris Oates
| A Bayes-Sard Cubature Method | 2018 |
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Professor Chris Oates
| Graphical Models in Molecular Systems Biology | 2018 |
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Professor Chris Oates
| On the Bayesian Solution of Differential Equations | 2018 |
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Professor Chris Oates
| Stein Points | 2018 |
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Professor Chris Oates
| Control functionals for Monte Carlo integration | 2017 |
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Professor Chris Oates
| Investigation of the Widely Applicable Bayesian Information Criteria | 2017 |
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Professor Chris Oates
| On the Sampling Problem for Kernel Quadrature | 2017 |
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Professor Chris Oates
| Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models | 2017 |
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Professor Chris Oates
| Repair of Partly Misspecified Causal Diagrams | 2017 |
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Professor Chris Oates
| Control Functionals for Quasi-Monte Carlo Integration | 2016 |
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Professor Chris Oates
| Estimating Causal Structure Using Conditional DAG Models | 2016 |
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Professor Chris Oates
| Exact Estimation of Multiple Directed Acyclic Graphs | 2016 |
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Professor Chris Oates
| Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods | 2016 |
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Professor Chris Oates
| Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems | 2016 |
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Professor Chris Oates
| RNA editing generates sequence diversity within cell populations | 2016 |
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Professor Chris Oates
| The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation | 2016 |
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Professor Chris Oates
| Accelerated Non-parametrics for Cascades of Poisson Processes | 2015 |
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Professor Chris Oates
| Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer | 2015 |
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Professor Chris Oates
| Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees | 2015 |
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Professor Chris Oates
| Towards a Multisubject Analysis of Neural Connectivity | 2015 |
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Professor Chris Oates
| A stochastic model dissects cell states in biological transition processes | 2014 |
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Professor Chris Oates
| Causal network inference using biochemical kinetics | 2014 |
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Professor Chris Oates
| Joint Estimation of Multiple Related Biological Networks | 2014 |
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Professor Chris Oates
Sunil Mukherjee
| Joint Structure Learning of Multiple Non-Exchangeable Networks | 2014 |
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Professor Chris Oates
| Quantifying the Multi-Scale Performance of Network Inference Algorithms | 2014 |
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Professor Chris Oates
| Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines | 2014 |
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