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| [UNTRANSLATED]× | Bayesian Kriging (Mudelit-põhine geostaistika)× | |
|---|---|---|
| Valdkond | Ruumianalüüs | Ruumianalüüs |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 1990s–2000s | 1993–1998 |
| Looja≠ | Gelfand, Banerjee & colleagues; building on Matheron's cokriging framework | Diggle, Tawn & Moyeed; Handcock & Stein |
| Tüüp | Bayesian spatial interpolation | Bayesian spatial interpolation |
| Algallikas≠ | Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079 | 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 ↗ |
| Rööpnimetused | Bayesian cokriging, Bayesian co-regionalization, BCK, Bayesian multivariate kriging | Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | Bayesian Co-Kriging is a multivariate geostatistical method that uses auxiliary spatially correlated variables to improve predictions of a primary variable of interest. By placing Bayesian priors on cross-covariance parameters, it propagates all uncertainty — including parameter uncertainty — into the prediction intervals, yielding fully probabilistic maps with calibrated uncertainty bounds. | 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. |
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