University of Sheffield

Tony O'Hagan - Academic pages - Abstracts


Bayesian Emulation of Complex Multi-Output and Dynamic Computer Models

Stefano Conti and Anthony O'Hagan

Department of Probability and Statistics, University of Sheffield, Sheffield, England

Publication details: Journal of Statistical Planning and Inference 140, 640-651, 2010.


Computer models are widely used in scientific research to study and predict the behaviour of complex systems. The run times of computer-intensive simulators are often such that it is impractical to make the thousands of model runs that are conventionally required for sensitivity analysis, uncertainty analysis or calibration. In response to this problem, highly efficient techniques have recently been developed based on a statistical model (the emulator) that is built to approximate the computer model. The approach, however, is less straightforward for dynamic simulators, designed to represent time-evolving systems. A generalisation of the established methodology to allow for dynamic emulation is here proposed. Advantages and difficulties are discussed and illustrated with an application to the Sheffield Dynamic Global Vegetation Model, developed within the UK Centre for Terrestrial Carbon Dynamics.

Keywords: Bayesian inference, computer experiments, dynamic models, hierarchical models

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Updated: 12 April 2010
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