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Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model

Konomi, B.; Karagiannis, G.

Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model Thumbnail


Authors

B. Konomi



Abstract

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.

Citation

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. https://doi.org/10.1080/00401706.2020.1855253

Journal Article Type Article
Acceptance Date Nov 16, 2020
Online Publication Date Dec 7, 2020
Publication Date 2021
Deposit Date Nov 16, 2020
Publicly Available Date Dec 7, 2021
Journal Technometrics
Print ISSN 0040-1706
Electronic ISSN 1537-2723
Publisher American Statistical Association
Peer Reviewed Peer Reviewed
Volume 63
Issue 4
Pages 510-522
DOI https://doi.org/10.1080/00401706.2020.1855253
Public URL https://durham-repository.worktribe.com/output/1250958
Related Public URLs https://arxiv.org/abs/1910.08063

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Accepted Journal Article (4.9 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by-nc/4.0/

Copyright 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.





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