Staff profile

• Applied statistics
• Bayesian Statistics
• Modelling uncertainty in complex systems
• Maths education in the early years

Conference Paper

• Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks
  
  Oughton, R., Wooff, D., Swarbrick, R., & Hobbs, R. (2015, June 1). Modelling Uncertainty in Pore Pressure Using Dynamic Bayesian Networks. Presented at 77th EAGE Conference & Exhibition 2015 : Earth Science for Energy and Environment., Madrid, Spain. https://doi.org/10.3997/2214-4609.201413296

Journal Article

• Developing ‘deep mathematical thinking’ in geometry with 3- and 4-year-olds: A collaborative study between early years teachers and University-based mathematicians
  
  Oughton, R., Nichols, K., Bolden, D. S., Dixon-Jones, S., Fearn, S., Darwin, S., Mistry, M., Peyerimhoff, N., & Townsend, A. (2024). Developing ‘deep mathematical thinking’ in geometry with 3- and 4-year-olds: A collaborative study between early years teachers and University-based mathematicians. Mathematical Thinking and Learning, 26(3), 306-325. https://doi.org/10.1080/10986065.2022.2119497
• Going round in circles: Geometry in the early years
  
  Oughton, R. H., Wheadon, D. M., Bolden, D. S., Nichols, K., Fearn, S., Darwin, S., Dixon-Jones, S., Mistry, M., Peyerimhoff, N., & Townsend, A. (2023). Going round in circles: Geometry in the early years. Mathematics Teaching, 286, 29-34.
• The effects of nutrition and health claims on the nutrient composition of single and subsequent meal servings
  
  Benson, T., Bucher, T., Oughton, R., McCloat, A., Mooney, E., Farrell, S., & Dean, M. (2022). The effects of nutrition and health claims on the nutrient composition of single and subsequent meal servings. Appetite, 176, Article 106105. https://doi.org/10.1016/j.appet.2022.106105
• Intermediate Variable Emulation: using internal processes in simulators to build more informative emulators
  
  Oughton, R., Goldstein, M., & Hemmings, J. (2022). Intermediate Variable Emulation: using internal processes in simulators to build more informative emulators. SIAM/ASA/Journal/on/Uncertainty/Quantification, 10(1), 268-293. https://doi.org/10.1137/20m1370902
• A sequential dynamic Bayesian network for pore pressure estimation with uncertainty quantification
  
  Oughton, R. H., Wooff, D. A., Hobbs, R. W., Swarbrick, R. E., & O’Connor, S. A. (2018). A sequential dynamic Bayesian network for pore pressure estimation with uncertainty quantification. Geophysics, 83(2), D27-D39. https://doi.org/10.1190/geo2016-0566.1
• A study of non-linearity in rainfall-runoff response using 120 UK catchments
  
  Mathias, S., McIntyre, N., & Oughton, R. (2016). A study of non-linearity in rainfall-runoff response using 120 UK catchments. Journal of Hydrology, 540, 423-436. https://doi.org/10.1016/j.jhydrol.2016.06.039
• Hierarchical Emulation: a method for modeling and comparing nested simulators
  
  Oughton, R. H., & Craig, P. S. (2016). Hierarchical Emulation: a method for modeling and comparing nested simulators. SIAM/ASA/Journal/on/Uncertainty/Quantification, 4(1), 495-519. https://doi.org/10.1137/15m1007914
• A Bayesian shifting method for uncertainty in the open-hole gamma-ray log around casing points
  
  Oughton, R. H., Wooff, D. A., & O’Connor, S. A. (2014). A Bayesian shifting method for uncertainty in the open-hole gamma-ray log around casing points. Petroleum Geoscience, 20(4), 375-391. https://doi.org/10.1144/petgeo2014-006

Report

• Independent evaluation of the Early Years Conversation Project (EYCP): A two-armed cluster randomised waitlist-controlled trial
  
  Qi, X., Menzies, V., Siddiqui, N., Ismail, N., & Oughton, R. (2023). Independent evaluation of the Early Years Conversation Project (EYCP): A two-armed cluster randomised waitlist-controlled trial. Education Endowment Foundation.
• Quantifying uncertainty in pore pressure estimation using Bayesian networks, with application to use of an offset well
  
  Oughton, R., Wooff, D., Hobbs, R., O’Connor, S., & Swarbrick, R. (2015). Quantifying uncertainty in pore pressure estimation using Bayesian networks, with application to use of an offset well. https://doi.org/10.3997/2214-4609.201413638