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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. and Karagiannis, G. 'Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: application to the WRF model.', Technometrics. .

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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1080/00401706.2020.1855253
Supplementary material:https://doi.org/10.6084/m9.figshare.13341474.v1
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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