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Symulacja przestrzennego bootstrapu×Przestrzenne wnioskowanie bayesowskie×
DziedzinaStatystyka bayesowskaStatystyka bayesowska
RodzinaBayesian methodsBayesian methods
Rok powstania1990s–2000s1991
TwórcaLahiri and others, building on Efron's bootstrap (1979)Besag, York & Mollie (CAR prior, 1991); Gelfand & colleagues (Bayesian geostatistics, 1990s)
TypResampling / simulationBayesian hierarchical spatial model
Źródło pierwotneLahiri, S. N. (2003). Resampling Methods for Dependent Data. Springer. ISBN: 978-0387009285Banerjee, S., Carlin, B. P. & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173
Inne nazwyspatial block bootstrap, spatial resampling, geostatistical bootstrap, bootstrap for spatial dataBayesian spatial analysis, Bayesian geostatistics, spatial Bayesian modeling, Bayesian areal modeling
Pokrewne42
PodsumowanieSpatial 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.
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ScholarGatePorównaj metody: Spatial Bootstrap Simulation · Spatial Bayesian Inference. Pobrano 2026-06-15 z https://scholargate.app/pl/compare