An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive interpretation of the field properties. On the computational side, GFs are hampered with the big n problem, since the cost of factorizing dense matrices is cubic in the dimension. Although computatio...

An explicit link between Gaussian fields and Gaussian Markov random fields: The SPDE approach

Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive interpretation of the field properties. On the computational side, GFs are hampered with the big n problem, since the cost of factorising dense matrices is cubic in the dimension. Although the computa...

NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET An explicit link between Gaussian fields and Gaussian Markov random fields: The SPDE approach

Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial statistical modelling and geo-statistics. The specification through the covariance function gives an intuitive interpretation of its properties. On the computational side, GFs are hampered with the big-n problem, since the cost of factorising dense matrices is cubic in the dimension. Although the computationa...

An Explicit Link between Gaussian Fields And

Continuously indexed Gaussian fields (GFs) is the most important ingredient in spatial statistical modelling and geo-statistics. The specification through the covariance function gives an intuitive interpretation of its properties. On the computational side, GFs are hampered with the big-n problem, since the cost of factorising dense matrices is cubic in the dimension. Although the computationa...

Gaussian Markov Random Fields: Theory and Applications