Methoden vergleichen
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| Bayesian Kriging (Modellbasierte Geostatistik)× | Universelles Kriging (Kriging mit Trend)× | |
|---|---|---|
| Fachgebiet | Räumliche Analyse | Räumliche Analyse |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1993–1998 | 1969 |
| Urheber≠ | Diggle, Tawn & Moyeed; Handcock & Stein | Georges Matheron |
| Typ≠ | Bayesian spatial interpolation | Geostatistical interpolation with spatial trend |
| Wegweisende Quelle≠ | Diggle, P. J., Tawn, J. A., & Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(3), 299–350. DOI ↗ | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. DOI ↗ |
| Aliasnamen | Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging | kriging with a trend, kriging with drift, trend kriging, evrensel kriging |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | Bayesian Kriging embeds classical geostatistical interpolation inside a full probabilistic framework. Instead of treating variogram parameters as fixed point estimates, it places prior distributions on them and updates these priors with observed spatial data to obtain a posterior distribution. Predictions at unsampled locations are then marginalised over this uncertainty, yielding honest predictive intervals that account for both spatial dependence and parameter uncertainty. | Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling with latitude or a pollutant gradient, while still returning prediction variances. |
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