Bayesian spatial modelling of soil properties and their uncertainty: The example of soil organic matter in Scotland using R-INLA

By Laura Poggio, Alessandro Gimona, L. Spezia, M. Brewer. 2016

58 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/bd7af99148dc650bca03e2ea907d59a620bbb2bb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Andrea Riebler
2. Daniel P. Simpson
3. Finn Lindgren
4. Håvard Rue
5. H. Bakka
6. David Bolin
7. Geir-Arne Fuglstad
8. Elias Teixeira Krainski

In Environmental Engineering

1. Daniel P. Simpson
2. Finn Lindgren
3. Håvard Rue
4. Andrea Riebler
5. David Bolin
6. Geir-Arne Fuglstad
7. H. Bakka
8. Elias Teixeira Krainski

Citations in the corpus

• Spatial modelling with R-INLA: A review (2018, background): “…locations this may lead to long computation times (Huang et al., 2017) and while it can be reduced somewhat by re-running the model multiple times with disjoint subsets of the desired prediction locations (Poggio et al., 2016), a better approach is to use i.i.d. samples from the joint posterior.”