Gaussian Markov Random Fields: Theory and Applications

By Jun Yan. 2006

86 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ed5cd968324163a12cc8d9d06b7a47c4a02473fc

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. Finn Lindgren
2. Ingelin Steinsland
3. R. Ingebrigtsen
4. Sara Martino
5. Håvard Rue
6. Daniel P. Simpson
7. Andrea Riebler
8. Thomas Opitz

In Statistics and Probability

1. Finn Lindgren
2. Ingelin Steinsland
3. R. Ingebrigtsen
4. Sara Martino
5. Håvard Rue
6. Francky Fouedjio
7. Daniel P. Simpson
8. Andrea Riebler

Citations in the corpus

• Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field (2014, background): “This means that the spatial model itself is defined in continuous space, while the computations can be carried out efficiently due to properties of discrete GMRFs (see e.g. Rue and Held, 2005).”