Prof Jeremy Oakley

Position: Professor
Home page:
Telephone: (0114) 2223853
Office: I16 Hicks building
Photo of Jeremy Oakley


MAS113 Introduction to Probability and Statistics Information Home page (also MOLE)
MAS281 Probability and Statistics in Society Information uses MOLE
MAS6005 Professional Skills for Statisticians Information uses MOLE
MAS6006 Statistical Consultancy Information uses MOLE
MAS6010 Data Analysis Information  


Interests: Bayesian statistics; eliciting probability distributions; health economics; uncertainty quantification for complex computer models
Research group: Statistics
Publications: MathSciNet


Current grants, as Principal Investigator
Bayesian estimation of subgroup effects
Current grants, as Coinvestigator
Uncertainty Quantification in Prospective and Predictive Patient Specific Cardiac Models EPSRC
Past grants, as Principal Investigator
Gaussian Process Emulators for Numerical Models
Simulation Tools for Automated and Robust Manufacturing EPSRC
Past grants, as Coinvestigator
Calibration and analysis of complex models: methodological development and application to explore the impact of HAART in Africa
Managing Uncertainty in Complex Models 2 (MUCM2) EPSRC
Managing Uncertainty in Complex Models EPSRC
Coupled models: Expert judgement, emulators and model uncertainty EPSRC
Simplicity, complexity and modelling EPSRC
BAMRA: Bayesian approaches in microbial risk assessment (Working group) NERC
Mathematics for data modelling EPSRC
The probability of rapid climate change NERC
The probability of rapid climate change NERC


Professor Oakley obtained his BSc (1996) in Mathematics and Statistics from the University of Nottingham, and his PhD (2000) in Statistics from the University of Sheffield. He has worked as a post- doctoral research associate in both the Department of Computer Science and Department of Probability and Statistics, University of Sheffield, before starting a lectureship in Probability and Statistics in 2002. He has various research interests in Bayesian statistics including uncertainty quantification for computer models, prior elicitation and Bayesian methods in health economics.