विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बायेसियन क्रिगिंग (मॉडल-आधारित भू-सांख्यिकी)× | यूनिवर्सल क्रिगिंग (प्रवृत्ति के साथ क्रिगिंग)× | |
|---|---|---|
| क्षेत्र | स्थानिक विश्लेषण | स्थानिक विश्लेषण |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1993–1998 | 1969 |
| प्रवर्तक≠ | Diggle, Tawn & Moyeed; Handcock & Stein | Georges Matheron |
| प्रकार≠ | Bayesian spatial interpolation | Geostatistical interpolation with spatial trend |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम | Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging | kriging with a trend, kriging with drift, trend kriging, evrensel kriging |
| संबंधित≠ | 5 | 3 |
| सारांश≠ | 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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