Confronta i metodi

	Kriging Bayesiano (Geostatistica basata su modello)	Co-kriging: Interpolazione Geostatistica Multivariata
Campo	Analisi spaziale	Analisi spaziale
Famiglia	Regression model	Regression model
Anno di origine	1993–1998	1965-1978
Ideatore	Diggle, Tawn & Moyeed; Handcock & Stein	Matheron, G.; extended by Journel & Huijbregts
Tipo	Bayesian spatial interpolation	Geostatistical interpolation
Fonte seminale	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.	Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London. ISBN: 978-0123910561
Alias	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging	cokriging, co-regionalization kriging, multivariate kriging, CK
Correlati	5	5
Sintesi	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.	Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable is more densely sampled or spatially correlated with the primary variable of interest.