Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-2009-2019
https://doi.org/10.5194/gmd-12-2009-2019
Methods for assessment of models
 | 
23 May 2019
Methods for assessment of models |  | 23 May 2019

Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

Ahmed S. Elshall, Ming Ye, Guo-Yue Niu, and Greg A. Barron-Gafford

Viewed

Total article views: 2,343 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,554 736 53 2,343 147 53 46
  • HTML: 1,554
  • PDF: 736
  • XML: 53
  • Total: 2,343
  • Supplement: 147
  • BibTeX: 53
  • EndNote: 46
Views and downloads (calculated since 26 Nov 2018)
Cumulative views and downloads (calculated since 26 Nov 2018)

Viewed (geographical distribution)

Total article views: 2,343 (including HTML, PDF, and XML) Thereof 2,061 with geography defined and 282 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Apr 2024
Download
Short summary
The assumptions that the residuals are independent, identically distributed, and have constant variance tend to simplify the underlying mathematics of data models for Bayesian inference. We relax these three assumptions step-wise, resulting in eight data models. Using three mechanistic soil respiration models with different levels of model discrepancy, we discuss the impacts of data models on parameter estimation and predictive performance, and provide recommendations for data model selection.