Methoden vergleichen
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| Bayesian Kernel Density Estimation× | Lokales Kriging (Moving-Window Kriging)× | |
|---|---|---|
| Fachgebiet | Räumliche Analyse | Räumliche Analyse |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1995 | 1990 |
| Urheber≠ | Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics | Haas, T. C. |
| Typ≠ | Nonparametric density estimation | Spatial interpolation (local variant) |
| Wegweisende Quelle≠ | Hjort, N. L., & Glad, I. K. (1995). Nonparametric density estimation with a parametric start. The Annals of Statistics, 23(3), 882–904. DOI ↗ | Haas, T. C. (1990). Kriging and automated variogram modeling within a moving window. Atmospheric Environment, 24(7), 1759-1769. DOI ↗ |
| Aliasnamen | Bayesian KDE, BKDE, Bayesian nonparametric density estimation, Bayesian adaptive KDE | moving-window kriging, local kriging interpolation, windowed kriging, neighborhood kriging |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | Bayesian Kernel Density Estimation (BKDE) is a nonparametric method for estimating the probability density function of a spatial or attribute variable by combining a kernel smoother with a Bayesian prior over the bandwidth parameter. The posterior distribution of the bandwidth propagates uncertainty into the final density estimate rather than treating the bandwidth as a fixed tuning constant. | Local Kriging is a spatially adaptive geostatistical interpolation method that restricts each prediction to a moving neighborhood of nearby observations, fitting a variogram model locally within that window. This allows spatial covariance structure to vary across the study region rather than imposing a single global variogram, making it better suited to large or non-stationary spatial fields. |
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