Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 7.49 MB
Add to list
  1. Search results
  2. Comparing well and geophysical data f...

Comparing well and geophysical data for temperature monitoring within a Bayesian Experimental Design framework

Robin Thibaut, Nicolas Compaire, Nolwenn Lesparre, Maximilian Ramgraber, Eric Laloy and Thomas Hermans (UGent)
(2022) Computational Methods in Water Resources, Abstracts.
Author
Organization
Project

Downloads

  • Download
    thibaut robin.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 7.49 MB

Citation

Please use this url to cite or link to this publication:

MLA
Thibaut, Robin, et al. “Comparing Well and Geophysical Data for Temperature Monitoring within a Bayesian Experimental Design Framework.” Computational Methods in Water Resources, Abstracts, 2022.
APA
Thibaut, R., Compaire, N., Lesparre, N., Ramgraber, M., Laloy, E., & Hermans, T. (2022). Comparing well and geophysical data for temperature monitoring within a Bayesian Experimental Design framework. Computational Methods in Water Resources, Abstracts. Presented at the XXIV International Conference on Computational Methods in Water Resources (CMWR), Gdansk.
Chicago author-date
Thibaut, Robin, Nicolas Compaire, Nolwenn Lesparre, Maximilian Ramgraber, Eric Laloy, and Thomas Hermans. 2022. “Comparing Well and Geophysical Data for Temperature Monitoring within a Bayesian Experimental Design Framework.” In Computational Methods in Water Resources, Abstracts.
Chicago author-date (all authors)
Thibaut, Robin, Nicolas Compaire, Nolwenn Lesparre, Maximilian Ramgraber, Eric Laloy, and Thomas Hermans. 2022. “Comparing Well and Geophysical Data for Temperature Monitoring within a Bayesian Experimental Design Framework.” In Computational Methods in Water Resources, Abstracts.
Vancouver
1.
Thibaut R, Compaire N, Lesparre N, Ramgraber M, Laloy E, Hermans T. Comparing well and geophysical data for temperature monitoring within a Bayesian Experimental Design framework. In: Computational Methods in Water Resources, Abstracts. 2022.
IEEE
[1]
R. Thibaut, N. Compaire, N. Lesparre, M. Ramgraber, E. Laloy, and T. Hermans, “Comparing well and geophysical data for temperature monitoring within a Bayesian Experimental Design framework,” in Computational Methods in Water Resources, Abstracts, Gdansk, 2022.
@inproceedings{8759538,
  author       = {{Thibaut, Robin and Compaire, Nicolas and Lesparre, Nolwenn and Ramgraber, Maximilian and Laloy, Eric and Hermans, Thomas}},
  booktitle    = {{Computational Methods in Water Resources, Abstracts}},
  language     = {{eng}},
  location     = {{Gdansk}},
  pages        = {{20}},
  title        = {{Comparing well and geophysical data for temperature monitoring within a Bayesian Experimental Design framework}},
  year         = {{2022}},
}