방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 베이지안 크리깅 (모델 기반 지리통계학)× | 정규 크리깅× | |
|---|---|---|
| 분야 | 공간분석 | 공간분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1993–1998 | 1963 |
| 창시자≠ | Diggle, Tawn & Moyeed; Handcock & Stein | Georges Matheron (formalising D.G. Krige's empirical work) |
| 유형≠ | Bayesian spatial interpolation | Geostatistical interpolation |
| 원전≠ | 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 ↗ |
| 별칭 | Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging | OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | Ordinary Kriging (OK) is the standard geostatistical method for interpolating a continuous spatial variable at unsampled locations. It derives optimal, unbiased weights from the spatial covariance structure of the data, making it the Best Linear Unbiased Predictor (BLUP) under stationarity assumptions. Unlike simpler distance-based methods, it also provides a prediction uncertainty (kriging variance) at every interpolated point. |
| ScholarGate데이터셋 ↗ |
|
|