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| Bayesianisches Universelles Kriging× | Co-Kriging: Multivariate Geostatistische Interpolation× | |
|---|---|---|
| Fachgebiet | Räumliche Analyse | Räumliche Analyse |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1990s–2000s | 1965-1978 |
| Urheber≠ | Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein | Matheron, G.; extended by Journel & Huijbregts |
| Typ≠ | Bayesian geostatistical interpolation with trend | Geostatistical interpolation |
| Wegweisende Quelle≠ | Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079 | Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561 |
| Aliasnamen | BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging | cokriging, co-regionalization kriging, multivariate kriging, CK |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Bayesian Universal Kriging (BUK) extends classical universal kriging by placing prior distributions on trend coefficients and spatial covariance parameters, then propagating full posterior uncertainty into predictions. It interpolates spatially referenced continuous data while simultaneously estimating large-scale deterministic trends driven by covariates and small-scale stochastic spatial dependence, yielding prediction intervals that honestly account for both parameter and interpolation uncertainty. | Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable is more densely sampled or spatially correlated with the primary variable of interest. |
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