Comparer des méthodes

	Co-Krigage Bayésien	Krigage universel bayésien
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1990s–2000s	1990s–2000s
Auteur d'origine	Gelfand, Banerjee & colleagues; building on Matheron's cokriging framework	Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein
Type	Bayesian spatial interpolation	Bayesian geostatistical interpolation with trend
Source fondatrice	Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079	Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079
Alias	Bayesian cokriging, Bayesian co-regionalization, BCK, Bayesian multivariate kriging	BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging
Apparentées	5	6
Résumé	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 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.