Konomi, B. and Karagiannis, G. (2021) 'Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: application to the WRF model.', Technometrics., 63 (4). pp. 510-522.
Motivated by a multi-fidelity Weather Research and Forecasting (WRF) climate model application where the available simulations are not generated based on hierarchically nested experimental design, we develop a new co-kriging procedure called Augmented Bayesian Treed Co-Kriging. The proposed procedure extends the scope of co-kriging in two major ways. We introduce a binary treed partition latent process in the multifidelity setting to account for non-stationary and potential discontinuities in the model outputs at different fidelity levels. Moreover, we introduce an efficient imputation mechanism which allows the practical implementation of co-kriging when the experimental design is non-hierarchically nested by enabling the specification of semi-conjugate priors. Our imputation strategy allows the design of an efficient RJ-MCMC implementation that involves collapsed blocks and direct simulation from conditional distributions. We develop the Monte Carlo recursive emulator which provides a Monte Carlo proxy for the full predictive distribution of the model output at each fidelity level, in a computationally feasible manner. The performance of our method is demonstrated on benchmark examples and used for the analysis of a large-scale climate modeling application which involves the WRF model.
|Full text:||(AM) Accepted Manuscript|
Available under License - Creative Commons Attribution Non-commercial 4.0.
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|Publisher Web site:||https://doi.org/10.1080/00401706.2020.1855253|
|Publisher statement:||This is an Accepted Manuscript version of the following article, accepted for publication in Technometrics. Konomi, B. & Karagiannis, G. (2021). Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model. Technometrics 63(4): 510-522. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.|
|Date accepted:||16 November 2020|
|Date deposited:||16 November 2020|
|Date of first online publication:||07 December 2020|
|Date first made open access:||07 December 2021|
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