Comparer des méthodes

	Simulation par bootstrap spatial	MCMC spatiale
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1990s–2000s	1990s
Auteur d'origine	Lahiri and others, building on Efron's bootstrap (1979)	Gelfand, Smith, and colleagues (early 1990s MCMC for spatial models)
Type	Resampling / simulation	Bayesian computational method
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	spatial Markov chain Monte Carlo, MCMC for spatial data, spatial Bayesian MCMC, geostatistical MCMC
Apparentées	4	4
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 MCMC applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for spatial dependence among observations. It draws posterior samples from models such as conditional autoregressive (CAR), simultaneous autoregressive (SAR), or geostatistical (Gaussian process) models, yielding full uncertainty distributions for spatially structured parameters like random effects, regression coefficients, and spatial range.