Bayes Linear Emulation of Simulated Crop Yield

Hasan, Muhammad Mahmudul; Cumming, Jonathan A.

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
• Public URL: https://durham-repository.worktribe.com/output/1622714
• Related Public URLs: https://arxiv.org/abs/2109.10208