« Régression par processus gaussien » : différence entre les versions

Version du 2 novembre 2021 à 16:29

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kriging

Gaussian process regression

Source : Wikipedia Machine Learning

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 '''Gaussian process regression'''
-In statistics, originally in geostatistics, kriging or Gaussian process regression is a method of interpolation for which the interpolated values are modeled by a Gaussian process governed by prior covariances. Under suitable assumptions on the priors, kriging gives the best linear unbiased prediction of the intermediate values.[citation needed] Interpolating methods based on other criteria such as smoothness (e.g., smoothing spline) may not yield the most likely intermediate values. The method is widely used in the domain of spatial analysis and computer experiments. The technique is also known as Wiener–Kolmogorov prediction, after Norbert Wiener and Andrey Kolmogorov.
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