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Think continuous : Markovian Gaussian models in spatial statistics


Reference:

Simpson, D., Lindgren, F. and Rue, H., 2012. Think continuous : Markovian Gaussian models in spatial statistics. Spatial Statistics, 1, pp. 16-29.

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Official URL:

http://dx.doi.org/10.1016/j.spasta.2012.02.003

Abstract

Gaussian Markov random fields (GMRFs) are frequently used as computationally efficient models in spatial statistics. Unfortunately, it has traditionally been difficult to link GMRFs with the more traditional Gaussian random field models, as the Markov property is difficult to deploy in continuous space. Following the pioneering work of Lindgren etal. (2011), we expound on the link between Markovian Gaussian random fields and GMRFs. In particular, we discuss the theoretical and practical aspects of fast computation with continuously specified Markovian Gaussian random fields, as well as the clear advantages they offer in terms of clear, parsimonious, and interpretable models of anisotropy and non-stationarity.

Details

Item Type Articles
CreatorsSimpson, D., Lindgren, F. and Rue, H.
DOI10.1016/j.spasta.2012.02.003
DepartmentsFaculty of Science > Mathematical Sciences
RefereedYes
StatusPublished
ID Code32286

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