Comparar métodos

	Bayesian Co-Kriging	Kriging Ordinario
Campo	Análisis espacial	Análisis espacial
Familia	Regression model	Regression model
Año de origen	1990s–2000s	1963
Autor original	Gelfand, Banerjee & colleagues; building on Matheron's cokriging framework	Georges Matheron (formalising D.G. Krige's empirical work)
Tipo	Bayesian spatial interpolation	Geostatistical interpolation
Fuente seminal	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.
Alias	Bayesian cokriging, Bayesian co-regionalization, BCK, Bayesian multivariate kriging	OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor
Relacionados	5	4
Resumen	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.