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Übersetzung für 'kriging' von Englisch nach Deutsch
 geol.stat. kriging Kriging {n}
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⇄ Übersetzung für 'kriging' von Englisch nach Deutsch

kriging
Kriging {n}geol.stat.
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• Kriging {n} = kriging
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Anwendungsbeispiele Englisch
• kernels, RBFs or kriging. One such topic is response surface methodology.
• Should the variable be binary or represent classes of values, one is then talking about "indicator variograms". Indicator variogram is used in indicator kriging.
• Ying Hung is a Taiwanese-American statistician whose research centers on computer experiments, the use of the design of experiments to plan scientific and engineering simulations, and includes work on kriging, metamodeling, and the use of computer optimization techniques in the design of experiments.
• In the context of Gaussian models, especially in geostatistics, prediction using the best predictor, i.e. mean conditional on the data, is also known as kriging.

• Alternatives to LUR include kriging, atmospheric dispersion modeling and Bayesian Maximum Entropy modeling.
• The technique of kriging is named after him. Krige's empirical work to evaluate mineral resources was formalised in the 1960s by French engineer Georges Matheron.
• Gradient-enhanced kriging (GEK) is a surrogate modeling technique used in engineering.
• It is also possible to use an estimation by block models using several methods for estimating from the inverse of the square of the distance to more sophisticated as kriging or conditional simulation.
• (also known as kriging), where any combination of output points is assumed to be distributed as a multivariate Gaussian distribution.

• Algorithms of this type include multi-task learning (also called multi-output learning or vector-valued learning), transfer learning, and co-kriging.
• In statistics, originally in geostatistics, kriging or Kriging, also known as Gaussian process regression, is a method of interpolation based on Gaussian process governed by prior covariances.
• 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 (kriging) of the regression residuals.
• The Gaussian process regressions used on the latent level (the second stage) eventually produce kriging predictors for the curve parameters [...] that dictate the shape of the mean curve [...] on the date level (the first level).
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