Comparer des méthodes

	Simulation par bootstrap spatial	Inférence bayésienne spatiale
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1990s–2000s	1991
Auteur d'origine	Lahiri and others, building on Efron's bootstrap (1979)	Besag, York & Mollie (CAR prior, 1991); Gelfand & colleagues (Bayesian geostatistics, 1990s)
Type	Resampling / simulation	Bayesian hierarchical spatial model
Source fondatrice	Lahiri, S. N. (2003). Resampling Methods for Dependent Data. Springer. ISBN: 978-0387009285	Banerjee, S., Carlin, B. P. & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173
Alias	spatial block bootstrap, spatial resampling, geostatistical bootstrap, bootstrap for spatial data	Bayesian spatial analysis, Bayesian geostatistics, spatial Bayesian modeling, Bayesian areal modeling
Apparentées	4	2
Résumé	Spatial bootstrap simulation is a resampling technique designed for spatially dependent data. By resampling contiguous spatial blocks rather than independent observations, it preserves the local autocorrelation structure of the data and yields valid estimates of sampling variability for statistics computed on geographic or lattice observations.	Spatial Bayesian inference applies Bayesian hierarchical modeling to data indexed by geographic location. By placing structured spatial priors on location-specific random effects, the model borrows information from neighboring regions or nearby points, producing smooth, uncertainty-quantified maps of any spatially varying outcome — disease rates, pollution levels, species abundance, or environmental risk.