Emulation and History Matching Using the hmer Package
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 e...
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2024-06-01
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| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4848 |
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| author | Andrew Iskauskas Ian Vernon Michael Goldstein Danny Scarponi Nicky McCreesh Trevelyan J. McKinley Richard G. White |
| author_facet | Andrew Iskauskas Ian Vernon Michael Goldstein Danny Scarponi Nicky McCreesh Trevelyan J. McKinley Richard G. White |
| author_sort | Andrew Iskauskas |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-aa23d1c2d16e43859aed752b8d6cd03e |
| institution | Kabale University |
| issn | 1548-7660 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-aa23d1c2d16e43859aed752b8d6cd03e2024-12-29T00:12:43ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602024-06-01109110.18637/jss.v109.i10Emulation and History Matching Using the hmer PackageAndrew Iskauskas0Ian Vernon1Michael Goldstein2Danny Scarponi3Nicky McCreesh4Trevelyan J. McKinley5Richard G. White6Durham UniversityDurham UniversityDurham UniversityLondon School of Hygiene and Tropical MedicineLondon School of Hygiene and Tropical MedicineUniversity of ExeterLondon School of Hygiene and Tropical Medicine 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. https://www.jstatsoft.org/index.php/jss/article/view/4848 |
| spellingShingle | Andrew Iskauskas Ian Vernon Michael Goldstein Danny Scarponi Nicky McCreesh Trevelyan J. McKinley Richard G. White Emulation and History Matching Using the hmer Package Journal of Statistical Software |
| title | Emulation and History Matching Using the hmer Package |
| title_full | Emulation and History Matching Using the hmer Package |
| title_fullStr | Emulation and History Matching Using the hmer Package |
| title_full_unstemmed | Emulation and History Matching Using the hmer Package |
| title_short | Emulation and History Matching Using the hmer Package |
| title_sort | emulation and history matching using the hmer package |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4848 |
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