Research interests

Analysing uncertainty in complex computer models

Computer models (also known as process models, mechanistic models, simulation models etc.) are used widely throughout science and engineering for making predictions, and for conducting 'virtual experiments' when physical experiments would be too costly or impractical. There will almost always be uncertainty in any model prediction, caused by uncertainty about what input values to use, and/or uncertainty about how well the model represents reality. We cannot trust a computer model prediction until we have quantified the uncertainty properly.

My interest in this topic began with my PhD, which was on propagating input uncertainty through computationally expensive models, using Gaussian process emulators. I continue to work on work on methods for dealing with input uncertainty, though I think the most important problems now are to do with how we quantify uncertainty about model discrepancy: the difference between a model prediction and reality.

Uncertainty Quantification Research Group

Further reading and publications (show/hide)

Eliciting probability distributions from experts

Eliciting a probability distribution is the process of extracting an expert's beliefs about some unknown quantity of interest, and representing his/her beliefs with a probability distribution. The challenges are, firstly, to help the expert consider uncertainty carefully, without being excessively overconfident or underconfident, and secondly, to find a way of constructing a full probability distribution based on a small number of simple probability judgements from the expert. Elicitation can be used to construct prior distributions in Bayesian inference, though my interest is more in situations where we are using expert judgement because there is no data.

Elicitation software and publications (show/hide)

Bayesian statistical problems in health economics

Health economics is concerned with assessing the cost-effectiveness of medical technologies. Computer models are often used to make cost-effectiveness estimates, and my main interest is in analysing uncertainty in cost-effectiveness model predictions. Other interests include eliciting utilities for different states of health, in particular the design and analysis of discrete choice surveys. I collaborate with researchers in ScHARR through the Centre for Bayesian Statistics in Health Economics.

Publications (show/hide)