Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский универсальный кригинг× | Кокригинг: Многомерная геостатистическая интерполяция× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990s–2000s | 1965-1978 |
| Автор метода≠ | Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein | Matheron, G.; extended by Journel & Huijbregts |
| Тип≠ | Bayesian geostatistical interpolation with trend | Geostatistical interpolation |
| Основополагающий источник≠ | 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 |
| Другие названия | BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging | cokriging, co-regionalization kriging, multivariate kriging, CK |
| Связанные≠ | 6 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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