Working Papers

  • Fisher MA, Oates CJ, Powell C, Teckentrup A. A Locally Adaptive Bayesian Cubature Method. [arXiv]

  • South LF, Oates CJ, Mira M, Drovandi C. Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction. [arXiv]

  • Prüher J, Karvonen T, Oates CJ, Straka O, Särkkä S. Improved Calibration of Numerical Integration Error in Sigma-Point Filters. [arXiv]

  • Barp A, Oates CJ, Porcu E, Girolami M. A Riemannian-Stein Kernel Method. [arXiv]

  • Cockayne J, Oates CJ, Sullivan T, Girolami M. Probabilistic Meshless Methods for Bayesian Inverse Problems. [arXiv] [Video] [Poster] [ProbNum] [Video]



  • Karvonen T, Oates CJ, Särkkä S. A Bayes-Sard Cubature Method. Advances in Neural Information Processing Systems (NeurIPS 2018). [Journal] [arXiv]

  • Wang J, Cockayne J, Oates CJ. On the Bayesian Solution of Differential Equations. Proceedings of the 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. [arXiv]

  • Chen WY, Mackey L, Gorham J, Briol FX, Oates CJ. Stein Points. International Conference on Machine Learning (ICML 2018), Proceedings of Machine Learning Research, 80:844-853. [Journal] [arXiv]


  • Oates CJ, Niederer S, Lee A, Briol F-X, Girolami M. Probabilistic Models for Integration Error in Assessment of Functional Cardiac Models. Advances in Neural Information Processing Systems (NIPS 2017). [Journal] [arXiv] [Video] [Poster] [Blog]

  • Briol FX, Oates CJ, Cockayne J, Chen, WY, Girolami M. (2017) On the Sampling Problem for Kernel Quadrature. International Conference on Machine Learning (ICML 2017), Proceedings of Machine Learning Research, 70:586-595. [Journal] [arXiv]

  • Friel N, McKeone JP, Oates CJ, Pettitt AN. (2017) Discussion of "A Bayesian information criterion for singular models". Journal of the Royal Statistical Society (Series B), 79(2):323-380. [Journal] [arXiv]

  • Oates CJ, Kasza J, Simpson JA, Forbes AB. (2017) Repair of Partly Misspecified Causal Diagrams. Epidemiology, 28(4):548-552. [Journal] [PubMed] [Software] [Video] [Erratum]

  • Oates CJ, Girolami M, Chopin N. (2017) Control Functionals for Monte Carlo Integration. Journal of the Royal Statistical Society, Series B, 79(3):695-718. [Journal] [Web] [arXiv] [Blog1] [Blog2] [Supplement] [Software] [Software2]

  • Friel N, McKeone JP, Oates CJ, Pettitt AN. (2017) Investigation of the Widely Applicable Bayesian Information Criteria. Statistics and Computing, 27(3):833-844. [Journal] [arXiv] [Blog]


  • Cockayne J, Oates CJ, Sullivan T, Girolami M (2016) Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems. Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Ed. Geert Verdoolaege, AIP Conference Proceedings, 1853:060001. [Journal] [arXiv]

  • Oates CJ, Kasza J, Mukherjee S (2016) Discussion of “Causal inference using invariant prediction: identification and confidence intervals” by Peters, Bühlmann and Meinshausen. Journal of the Royal Statistical Society (Series B), 78(5):947-1012. [Journal] [arXiv]

  • Harjanto D, Papamarkou T, Oates CJ, Rayon Estrada V, Papavasiliou FN, Papavasiliou A. (2016) RNA editing generates sequence diversity within cell populations. Nature Communications, 7:12145. [Journal]

  • Oates CJ, Girolami M. (2016) Control Functionals for Quasi-Monte Carlo Integration. Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research W&CP, 51:56-65. [Journal] [arXiv] [Poster] [Selected for Oral Presentation; 6.5% of submissions]

  • Oates CJ, Smith JQ, Mukherjee S. (2016) Estimation of Causal Structure Using Conditional DAG Models. Journal of Machine Learning Research, 17(54):1−23. [Journal] [arXiv] [Supplement]

  • Oates CJ, Papamarkou T, Girolami M (2016) The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation. Journal of the American Statistical Association, 111(514):634-645. [Journal] [arXiv] [Blog1] [Blog2] [Blog3]

  • Friel N, Mira A, Oates CJ (2015) Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods. Bayesian Analysis, 11(1):215-245. [Journal] [arXiv] [Blog1] [Blog2]

  • Oates CJ, Smith JQ, Mukherjee S, Cussens J (2016) Exact Estimation of Multiple Directed Acyclic Graphs. Statistics and Computing, 26(4):797-811. [Journal] [arXiv] [Poster] [Software]


  • Briol F-X, Oates CJ, Girolami M, Osborne MA. (2015) Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees. Advances in Neural Information Processing Systems (NIPS 2015). [Journal] [arXiv] [Video[Blog1] [Blog2] [ProbNum] [Selected for Spotlight Presentation]

  • Korkola JE, Collisson EA, Heiser L, Oates CJ, Bayani N, Itani, S, Esch, A, Thompson, W, Griffith OL,Wang NJ, Kuo W-L, Cooper B, Billig J, Ziyad S, Hung JL, Jakkula L, Lu Y, Mills G, Spellman PT, Tomlin, C., Mukherjee S, Gray JW. (2015) Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer. PLoS One, 10(7):e0133219. [Journal]

  • Oates CJ. (2015) Accelerated Nonparametrics for Cascades of Poisson Processes. Stat, 4(1):183-195. [Journal] [arXiv] [Newsletter]

  • Oates CJ, Simpson D, Girolami M (2015) Discussion of “Sequential Quasi-Monte Carlo” by Gerber and Chopin. Journal of the Royal Statistical Society (Series B), 77(3):555-556. [Journal] [arXiv] [Blog]

  • Oates CJ, Costa L, Nichols T (2015) Towards a Multi-Subject Analysis of Neural Connectivity. Neural Computation, 27:151–170. [Journal] [arXiv]


  • Oates CJ, Amos R, Spencer SEF (2014) Quantifying the Multi-Scale Performance of Network Inference Algorithms. Statistical Applications in Genetics and Molecular Biology 13(5):611-631. [Journal] [arXiv] [Supplement]

  • Oates CJ, Korkola J, Gray, JW, Mukherjee S (2014) Joint Estimation of Multiple Related Biological Networks. The Annals of Applied Statistics 8(3):1892-1919. [Journal] [arXiv]

  • Oates CJ, Dondelinger F, Bayani N, Korkola J, Gray JW, Mukherjee S (2014) Causal network inference using biochemical kinetics. Bioinformatics 30(17):i468-i474. [Journal] [arXiv] [Software] [Best Paper Prize]

  • Oates CJ, Mukherjee S (2014) Joint Structure Learning of Multiple Non-Exchangeable Networks. Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), Journal of Machine Learning Research W&CP 33:687-695. [Journal] [arXiv]

  • Casale FP, Giurato G, Nassa G, Armond J, Oates CJ, Corà D, Gamba A, Mukherjee S, Weisz A, Nicodemi M (2014) Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines. PLoS One 9(2):e88485. [Journal]

  • Armond J, Saha K, Rana AA, Oates CJ, Jaenisch R, Nicodemi M, Mukherjee S (2014) A stochastic model dissects cellular states and heterogeneity in transition processes. Nature Scientific Reports 4:3692. [Journal] [WRAP]


  • Oates CJ (2013) Bayesian Inference for Protein Signalling Networks. PhD Thesis[pdf]

  • Chau Y-X, Oates CJ, Rana AA, Robinson L, Nicodemi M. (2013) Self Organisation and Emergence. In: Complexity Science: The Warwick Master’s Course (London Mathematical Society Lecture Note Series). Ed. by Ball R, Kolokoltsov V, MacKay R., Cambridge University Press. [Publisher]


  • Oates CJ, Mukherjee S (2012) Network Inference and Biological Dynamics. The Annals of Applied Statistics 6(3):1209-1235. [Journal] [arXiv]

  • Oates CJ, Hennessy BT, Lu Y, Mills GB, Mukherjee S (2012) Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics. Bioinformatics 28(18):2342-2348. [Journal]  [arXiv]