Prof. Jonathan Rougier
My research concerns uncertainty assessment in complex systems, particularly environmental systems such as weather and climate, and natural hazards. The main issues I think about are:
How do we use scientific models? If they are to be used quantatively to predict the behaviour of a system, e.g. the climate, how do we represent the discrepancy between the model and the system?
Is there a general framework for combining model-evaluations, system observations, and expert judgements, that is applicable across different areas of science?
What is the role of probability in representing our uncertainty? What does a probability represent, and what are the limitations of the probability calculus? What is the best way to explain probabilistic concepts to non-statisticians?
How do we implement our inferential calculations with very large models, that may take weeks or even months to evaluate? What corners do we cut?
Department of Mathematics, University of Bristol (since 2007)
Department of Mathematics, University of Durham (1997-2006)
Department of Economics, University of Durham (1990-1997)
Prudential Portfolio Managers (economist), (1988-1990)
BSc in Economics, University of Durham (1985-1988)
APTS module Statistical Inference, see here for details.
Theory of Inference, 10cp module at level 3/4, see here for details.
Bayesian Modelling B, 10cp module at level 3.
Previously at Bristol:
Statistics 2, 20cp module at level 2.
Multivariate Analysis, 10cp module at level 3/4.
Foundations of inference