Bayes Linear Emulation of Simulated Crop Yield

Hasan, Muhammad Mahmudul; Cumming, Jonathan A.

doi:10.1007/978-3-030-86133-9_7

Bayes Linear Emulation of Simulated Crop Yield

Hasan, Muhammad Mahmudul; Cumming, Jonathan A.

Authors

Muhammad Hasan muhammad.m.hasan@durham.ac.uk
PGR Student Doctor of Philosophy

Dr Jonathan Cumming j.a.cumming@durham.ac.uk
Associate Professor

Contributors

Yogendra P. Chaubey
Editor

Salim Lahmiri
Editor

Fassil Nebebe
Editor

Arusharka Sen
Editor

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.

Citation

Hasan, M. M., & Cumming, J. A. (2021). Bayes Linear Emulation of Simulated Crop Yield. In Y. P. Chaubey, S. Lahmiri, F. Nebebe, & A. Sen (Eds.), Applied Statistics and Data Science:Proceedings of Statistics 2021 Canada, Selected Contributions (145-151). Springer Verlag. https://doi.org/10.1007/978-3-030-86133-9_7

Online Publication Date	Feb 24, 2021
Publication Date	2021-02
Deposit Date	Jan 18, 2022
Publicly Available Date	Feb 24, 2022
Publisher	Springer Verlag
Pages	145-151
Series Title	Springer Proceedings in Mathematics & Statistics
Series Number	375
Book Title	Applied Statistics and Data Science:Proceedings of Statistics 2021 Canada, Selected Contributions
ISBN	978-3-030-86132-2
DOI	https://doi.org/10.1007/978-3-030-86133-9_7
Related Public URLs	https://arxiv.org/abs/2109.10208

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Copyright 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