On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models

Karagiannis, G.; Lin, G.

Abstract

For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. We propose the Bayesian calibration of computer model mixture method which relies on the idea of representing the real system output as a mixture of the available computer model outputs with unknown input dependent weight functions. The method builds a fully Bayesian predictive model as an emulator for the real system output by combining, weighting, and calibrating the available models in the Bayesian framework. Moreover, it fits a mixture of calibrated computer models that can be used by the domain scientist as a mean to combine the available computer models, in a flexible and principled manner, and perform reliable simulations. It can address realistic cases where one model may be more accurate than the others at different input values because the mixture weights, indicating the contribution of each model, are functions of the input. Inference on the calibration parameters can consider multiple computer models associated with different physics. The method does not require knowledge of the fidelity order of the models. We provide a technique able to mitigate the computational overhead due to the consideration of multiple computer models that is suitable to the mixture model framework. We implement the proposed method in a real-world application involving the Weather Research and Forecasting large-scale climate model.

Citation

Karagiannis, G., & Lin, G. (2017). On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models. Journal of Computational Physics, 342, 139-160. https://doi.org/10.1016/j.jcp.2017.04.003

• Journal Article Type: Article
• Acceptance Date: Apr 1, 2017
• Online Publication Date: Apr 25, 2017
• Publication Date: Aug 1, 2017
• Deposit Date: Jun 10, 2017
• Publicly Available Date: Apr 25, 2018
• Journal: Journal of Computational Physics
• Print ISSN: 0021-9991
• Electronic ISSN: 1090-2716
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 342
• Pages: 139-160
• DOI: https://doi.org/10.1016/j.jcp.2017.04.003
• Public URL: https://durham-repository.worktribe.com/output/1355789