Bayesian emulation of complex multi-output and dynamic computer models

Stefano Conti*, Anthony O'Hagan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

280 Citations (Scopus)


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 meta-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. Generalisations of the established methodology to allow for dynamic emulation are here proposed and contrasted. 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.

Original languageEnglish
Pages (from-to)640-651
Number of pages12
JournalJournal of Statistical Planning and Inference
Issue number3
Publication statusPublished - Mar 2010

Bibliographical note

Funding Information:
This research was supported by the Natural Environment Research Council through its funding for the Centre for Terrestrial Carbon Dynamics. The authors also wish to gratefully acknowledge Dr. Marc C. Kennedy for providing the data utilised in the application and two anonymous referees for their thoughtful comments on an earlier draft of the paper.


  • Bayesian inference
  • Computer experiments
  • Dynamic models
  • Hierarchical models


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