Discrepancy
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66085-6_12
Reference12 articles.
1. Bradley, W., Kim, J., Kilwein, Z., Blakely, L., Eydenberg, M., Jalvin, J., et al. (2022). Perspectives on the integration between first-principles and data-driven modeling. Computers & Chemical Engineering,166, 107898. https://doi.org/10.1016/j.compchemeng.2022.107898
2. Brynjarsdóttir, J., & O’Hagan, A. (2014). Learning about physical parameters: The importance of model discrepancy. Inverse Problems,30, 114007. https://doi.org/10.1088/0266-5611/30/11/114007
3. Cameron, D., Hartig, F., Minnuno, F., Oberpriller, J., Reineking, B., Van Oijen, M., & Dietze, M. (2022). Issues in calibrating models with multiple unbalanced constraints: The significance of systematic model and data errors. Methods in Ecology and Evolution, 2041–210X.14002. https://doi.org/10.1111/2041-210X.14002
4. Chandler, R. E. (2013). Exploiting strength, discounting weakness: Combining information from multiple climate simulators. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences,371, 20120388–20120388. https://doi.org/10.1098/rsta.2012.0388
5. Goldstein, M. (2011). External Bayesian analysis for computer simulators. Bayesian statistics,9, 201–228. https://doi.org/10.1093/acprof:oso/9780199694587.003.0007
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