Fast Spatial Prediction from Inhomogeneously Sampled Data Based on Generalized Random Fields with Gibbs Energy Functionals

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dc.creatorHristopulos, D. T.
dc.creatorElogne, S. N.
dc.date2006-09-08
dc.date.accessioned2026-07-07T07:26:04Z
dc.date.available2026-07-07T07:26:04Z
dc.descriptionAn explicit optimal linear spatial predictor is derived. The spatial correlations are imposed by means of Gibbs energy functionals with explicit coupling coefficients instead of covariance matrices. The model inference process is based on physically identifiable constraints corresponding to distinct terms of the energy functional. The proposed predictor is compared with the geostatistical linear optimal filter (kriging) using simulated data. The agreement between the two methods is excellent. The proposed framework allows a unified approach to the problems of parameter inference, spatial prediction and simulation of spatial random fields.
dc.description4 pages, 1 table
dc.identifierhttps://arxiv.org/abs/physics/0609071
dc.identifierhttp://arxiv.org/abs/physics/0609071
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/116944
dc.subjectData Analysis, Statistics and Probability
dc.subjectGeophysics
dc.titleFast Spatial Prediction from Inhomogeneously Sampled Data Based on Generalized Random Fields with Gibbs Energy Functionals
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