Emulation and History Matching using the hmer Package

Iskauskas, Andrew; Vernon, Ian; Goldstein, Michael; Scarponi, Danny; McKinley, Trevelyan J.; White, Richard G.; McCreesh, Nicky

Abstract

Modeling complex real-world situations such as infectious diseases, geological phenomena, and biological processes can present a dilemma: the computer model (referred to as a simulator) needs to be complex enough to capture the dynamics of the system, but each increase in complexity increases the evaluation time of such a simulation, making it difficult to obtain an informative description of parameter choices that would be consistent with observed reality. While methods for identifying acceptable matches to real-world observations exist, for example optimization or Markov chain Monte Carlo methods, they may result in non-robust inferences or may be infeasible for computationally intensive simulators. The techniques of emulation and history matching can make such determinations feasible, efficiently identifying regions of parameter space that produce acceptable matches to data while also providing valuable information about the simulator's structure, but the mathematical considerations required to perform emulation can present a barrier for makers and users of such simulators compared to other methods. The hmer package provides an accessible framework for using history matching and emulation on simulator data, leveraging the computational efficiency of the approach while enabling users to easily match to, visualize, and robustly predict from their complex simulators.

Citation

Iskauskas, A., Vernon, I., Goldstein, M., Scarponi, D., McKinley, T. J., White, R. G., & McCreesh, N. (2024). Emulation and History Matching using the hmer Package. Journal of Statistical Software, 109(10), 1–48. https://doi.org/10.18637/jss.v109.i10

• Journal Article Type: Article
• Acceptance Date: Dec 14, 2023
• Online Publication Date: Jun 3, 2024
• Publication Date: Jun 3, 2024
• Deposit Date: Dec 14, 2023
• Publicly Available Date: Jun 5, 2024
• Journal: Journal of Statistical Software
• Electronic ISSN: 1548-7660
• Publisher: Foundation for Open Access Statistic
• Peer Reviewed: Peer Reviewed
• Volume: 109
• Issue: 10
• Pages: 1–48
• DOI: https://doi.org/10.18637/jss.v109.i10
• Public URL: https://durham-repository.worktribe.com/output/2026282
• Related Public URLs: https://arxiv.org/abs/2209.05265