dorsal/arxiv
View SchemaFast Spatial Prediction from Inhomogeneously Sampled Data Based on Generalized Random Fields with Gibbs Energy Functionals
| Authors | D. T. Hristopulos, S. N. Elogne |
|---|---|
| Categories | |
| ArXiv ID | physics/0609071 |
| URL | https://arxiv.org/abs/physics/0609071 |
Abstract
An 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.
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"abstract": "An explicit optimal linear spatial predictor is derived. The spatial\ncorrelations are imposed by means of Gibbs energy functionals with explicit\ncoupling coefficients instead of covariance matrices. The model inference\nprocess is based on physically identifiable constraints corresponding to\ndistinct terms of the energy functional. The proposed predictor is compared\nwith the geostatistical linear optimal filter (kriging) using simulated data.\nThe agreement between the two methods is excellent. The proposed framework\nallows a unified approach to the problems of parameter inference, spatial\nprediction and simulation of spatial random fields.",
"arxiv_id": "physics/0609071",
"authors": [
"D. T. Hristopulos",
"S. N. Elogne"
],
"categories": [
"physics.data-an",
"physics.geo-ph"
],
"title": "Fast Spatial Prediction from Inhomogeneously Sampled Data Based on Generalized Random Fields with Gibbs Energy Functionals",
"url": "https://arxiv.org/abs/physics/0609071"
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