Comparar métodos

	Bayesian Co-Kriging	Kriging bayesiano (geoestadística basada en modelos)
Campo	Análisis espacial	Análisis espacial
Familia	Regression model	Regression model
Año de origen	1990s–2000s	1993–1998
Autor original	Gelfand, Banerjee & colleagues; building on Matheron's cokriging framework	Diggle, Tawn & Moyeed; Handcock & Stein
Tipo	Bayesian spatial interpolation	Bayesian spatial interpolation
Fuente seminal	Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079	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.
Alias	Bayesian cokriging, Bayesian co-regionalization, BCK, Bayesian multivariate kriging	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging
Relacionados	5	5
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.	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.