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Details of Grant 

EPSRC Reference: GR/K54557/01
Title: BAYESIAN UNCERTAINTY ANALYSIS AND CALIBRATION OF COMPLEX COMPUTER MODELS
Principal Investigator: O'hagan, Professor A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
National Radiological Protection Board
Department: Sch of Mathematical Sciences
Organisation: University of Nottingham
Scheme: Standard Research (Pre-FEC)
Starts: 01 October 1995 Ends: 30 September 1998 Value (£): 114,666
EPSRC Research Topic Classifications:
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
R&D
Related Grants:
Panel History:  
Summary on Grant Application Form
Complex computer models are widely used to describe and predict real-life processes, and often are costly and time-consuming to run. When one or more input parameters to the model are not precisely known when the model is used, uncertainty analysis consists of studying the resulting uncertainty about the output of the model. This research extends existing Bayesian theory for analysing data from experiments with computer models and for uncertainty analysis. The first part is to develop statistical models and Bayesian inference to incorporate the concept of model inadequacy, ie differences between the output of the computer model and the real-world phenomenon it is intended to represent. The second part is it develop theoretical and computational tools for the problem of calibrating such models, which concerns inference about unknown input parameters from real-world observations. Corresponding uncertainty analysis will be developed in both parts. In the first, model inadequacy is itself a component of uncertainty about predictions from the model. In the second, predictions must allow for residual, posterior, uncertainty about the input parameters after calibration.
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Organisation Website: http://www.nottingham.ac.uk