Сравнение методов

	Байесовский ко-кpигинг	Обычный кригинг
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	1990s–2000s	1963
Автор метода	Gelfand, Banerjee & colleagues; building on Matheron's cokriging framework	Georges Matheron (formalising D.G. Krige's empirical work)
Тип	Bayesian spatial interpolation	Geostatistical interpolation
Основополагающий источник	Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266.
Другие названия	Bayesian cokriging, Bayesian co-regionalization, BCK, Bayesian multivariate kriging	OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor
Связанные	5	4
Сводка	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.	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.