« Régression par processus gaussien » : différence entre les versions
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Voir '''[krigeage]''' | |||
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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. | 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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[https://en.wikipedia.org/wiki/Kriging Source : Wikipedia Machine Learning ] | [https://en.wikipedia.org/wiki/Kriging Source : Wikipedia Machine Learning ] | ||
[http://gdt.oqlf.gouv.qc.ca/ficheOqlf.aspx?Id_Fiche=26521561 Source : Le grand dictionnaire terminologique ] | |||
[[Catégorie:vocabulary]] | [[Catégorie:vocabulary]] | ||
[[Catégorie:Wikipedia-IA]] | [[Catégorie:Wikipedia-IA]] | ||
Version du 2 novembre 2021 à 15:28
Définition
Voir [krigeage]
Français
krigeage
Anglais
kriging
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.
Contributeurs: Amanda Clément, Claire Gorjux, Imane Meziani, wiki
