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Bayes Linear Emulation of Simulated Crop Yield

Hasan, Muhammad Mahmudul and Cumming, Jonathan A. (2021) 'Bayes Linear Emulation of Simulated Crop Yield.', in Applied Statistics and Data Science:Proceedings of Statistics 2021 Canada, Selected Contributions. Cham: Springer, pp. 145-151. Springer Proceedings in Mathematics & Statistics., 375

Abstract

The analysis of the output from a large-scale computer simulation experiment can pose a challenging problem in terms of size and computation. We consider output in the form of simulated crop yields from the Environmental Policy Integrated Climate (EPIC) model, which requires a large number of inputs—such as fertilizer levels, weather conditions, and crop rotations—inducing a high dimensional input space. In this paper, we adopt a Bayes linear approach to efficiently emulate crop yield as a function of the simulator fertilizer inputs. We explore emulator diagnostics and present the results from emulation of a subset of the simulated EPIC data output.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-030-86133-9_7
Publisher statement:This a post-peer-review, pre-copyedit version of a chapter published in Applied Statistics and Data Science Proceedings of Statistics 2021 Canada, Selected Contributions. The final authenticated version is available online at: https://doi.org/https://doi.org/10.1007/978-3-030-86133-9_7
Date accepted:No date available
Date deposited:19 January 2022
Date of first online publication:24 February 2021
Date first made open access:24 February 2022

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