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| Bayesian Kriging (Model-Based Geostatistics)× | Co-kriging: Wielowymiarowa interpolacja geostatystyczna× | |
|---|---|---|
| Dziedzina | Analiza przestrzenna | Analiza przestrzenna |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1993–1998 | 1965-1978 |
| Twórca≠ | Diggle, Tawn & Moyeed; Handcock & Stein | Matheron, G.; extended by Journel & Huijbregts |
| Typ≠ | Bayesian spatial interpolation | Geostatistical interpolation |
| Źródło pierwotne≠ | 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 ↗ | Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561 |
| Inne nazwy | Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging | cokriging, co-regionalization kriging, multivariate kriging, CK |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | 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. | 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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