I am a Professor of Statistics in the School of Mathematics and Statistics at the University of Sheffield. My research interests are in Bayesian statistics, in particular analysing uncertainty in complex computer models, eliciting probability distributions, and applications in Health Economics.
School of Mathematics and Statistics
The Hicks Building
+44 114 2223853
j.oakley at sheffield.ac.uk
Conference: Uncertainty in Computer Models 2014The UCM2014 conference will be held at the University of Sheffield, UK, during 28th-30th July 2014, on uncertainty in complex computer models, and is open to everyone interested in all aspects of computer model uncertainty, both theoretical and practical. The conference focuses on statistical methods to quantify and analyse the uncertainties in the predictions of computer models. Themes will include propagation of parameter uncertainty and sensitivity analysis; model structure uncertainty; Gaussian process emulators for computationally expensive models; inverse problems, model calibration and history matching. For further information, registration and abstract submission details, please visit the conference website.
- Oakley, J. E. and Youngman, B.D. Calibration of complex computer simulators using likelihood emulation. Submitted to Annals of Applied Statistics.
- Morris, D. E., Oakley, J. E. and Crowe, J. A. (2014). A web-based tool for eliciting probability distributions from experts. Environmental Modelling & Software, 52, 1-4.
- Ren, S. and Oakley, J. E. (2014). Assurance calculations for planning clinical trials with time-to-event outcomes. Statistics in Medicine 33(1), 31-45. Download supporting R code.
- Strong, M. and Oakley, J. E. (2014). When is a model good enough? Deriving the expected value of model improvement via specifying internal model discrepancies. SIAM/ASA Journal on Uncertainty Quantification, 2(1), 106-125.
- Strong, M, Oakley, J. E. and Brennan A. Estimating multi-parameter partial Expected Value of Perfect Information from a probabilistic sensitivity analysis sample: a non-parametric regression approach. To appear in Medical Decision Making.