Comparer des méthodes

	Calcul Bayésien Approché Spatial	Inférence bayésienne spatiale
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	2002 (spatial extensions from mid-2000s)	1991
Auteur d'origine	Diggle & Gratton (implicit statistical models, 1984); Beaumont, Zhang & Balding (ABC formalization, 2002)	Besag, York & Mollie (CAR prior, 1991); Gelfand & colleagues (Bayesian geostatistics, 1990s)
Type	likelihood-free Bayesian inference	Bayesian hierarchical spatial model
Source fondatrice	Beaumont, M. A., Zhang, W., & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.	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 ABC, ABC for spatial data, likelihood-free Bayesian spatial inference, simulation-based spatial inference	Bayesian spatial analysis, Bayesian geostatistics, spatial Bayesian modeling, Bayesian areal modeling
Apparentées	4	2
Résumé	Spatial Approximate Bayesian Computation (Spatial ABC) is a likelihood-free Bayesian inference framework for spatial data models whose likelihood function is intractable or too expensive to evaluate. It draws candidate parameters from a prior, simulates spatially structured datasets under those parameters, and accepts only the draws whose simulated spatial summary statistics closely match the observed data, thereby building an approximate posterior over model parameters.	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.