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IRFk-kriging assumes {()} to be an unknown polynomial in . Indicator kriging uses indicator functions instead of the process itself, in order to estimate transition probabilities. Multiple-indicator kriging is a version of indicator kriging working with a family of indicators. Initially, MIK showed considerable promise as a new method that ...
In applied statistics and geostatistics, regression-kriging (RK) is a spatial prediction technique that combines a regression of the dependent variable on auxiliary variables (such as parameters derived from digital elevation modelling, remote sensing/imagery, and thematic maps) with interpolation of the regression residuals.
Inference of continuous values with a Gaussian process prior is known as Gaussian process regression, or kriging; extending Gaussian process regression to multiple target variables is known as cokriging. [26] Gaussian processes are thus useful as a powerful non-linear multivariate interpolation tool. Kriging is also used to extend Gaussian ...
Kriging; Gradient-enhanced kriging (GEK) Thin plate spline; Polyharmonic spline (the thin-plate-spline is a special case of a polyharmonic spline) Radial basis function (Polyharmonic splines are a special case of radial basis functions with low degree polynomial terms) Least-squares spline; Natural neighbour interpolation
Simple Fourier based interpolation based on padding of the frequency domain with zero components (a smooth-window-based approach would reduce the ringing).Beside the good conservation of details, notable is the ringing and the circular bleeding of content from the left border to right border (and way around).
Gradient-enhanced kriging (GEK) is a surrogate modeling technique used in engineering. A surrogate model (alternatively known as a metamodel , response surface or emulator) is a prediction of the output of an expensive computer code. [ 1 ]
Radial basis function (RBF) interpolation is an advanced method in approximation theory for constructing high-order accurate interpolants of unstructured data, possibly in high-dimensional spaces.
One is thus making a distinction between the experimental variogram that is a visualization of a possible spatial/temporal correlation and the variogram model that is further used to define the weights of the kriging function. Note that the experimental variogram is an empirical estimate of the covariance of a Gaussian process.