scholarly journals Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes

2018 ◽  
Vol 25 (3) ◽  
pp. e2146 ◽  
Author(s):  
Sarah Osborn ◽  
Patrick Zulian ◽  
Thomas Benson ◽  
Umberto Villa ◽  
Rolf Krause ◽  
...  
Keyword(s):  
Random Fields ◽  
Spatially Correlated ◽  
Nonmatching Meshes

scholarly journals A Multilevel, Hierarchical Sampling Technique for Spatially Correlated Random Fields

2017 ◽  
Vol 39 (5) ◽  
pp. S543-S562 ◽  
Author(s):  
Sarah Osborn ◽  
Panayot S. Vassilevski ◽  
Umberto Villa
Keyword(s):  
Random Fields ◽  
Sampling Technique ◽  
Spatially Correlated ◽  
Hierarchical Sampling

Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas
Keyword(s):  
Spatial Correlation ◽  
Random Fields ◽  
Data Augmentation ◽  
Gaussian Random Fields ◽  
Classification Rule ◽  
Numerical Comparison ◽  
First Order ◽  
Bayesian Risk ◽  
Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

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