Think continuous: Markovian Gaussian models in spatial statistics

Details Think continuous: Markovian Gaussian models in spatial statistics Think continuous: Markovian Gaussian models in spatial statistics

2011-10-31

arxiv.org/abs/1110.6796v1

Mathematics

Statistics Theory

15 Pages, 5 Figures; 9/2011, Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU)

Scientific paper

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 et al. (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.

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