Comparer des méthodes

	Krigeage bayésien (géostatistique basée sur un modèle)	Régression Spatiale Bayésienne
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1993–1998	1990s–2000s
Auteur d'origine	Diggle, Tawn & Moyeed; Handcock & Stein	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors
Type	Bayesian spatial interpolation	Bayesian hierarchical regression
Source fondatrice	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.	Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173
Alias	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model
Apparentées	5	3
Résumé	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.	Bayesian Spatial Regression embeds a spatially structured random effect into a regression framework and estimates all parameters — including spatial range and variance — through posterior inference rather than point estimation. It handles spatial autocorrelation, quantifies full predictive uncertainty, and accommodates small or irregular spatial datasets via hierarchical priors.