Сравнение методов

	Пространственное бутстрэп-моделирование	Пространственный байесовский вывод
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1990s–2000s	1991
Автор метода	Lahiri and others, building on Efron's bootstrap (1979)	Besag, York & Mollie (CAR prior, 1991); Gelfand & colleagues (Bayesian geostatistics, 1990s)
Тип	Resampling / simulation	Bayesian hierarchical spatial model
Основополагающий источник	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
Другие названия	spatial block bootstrap, spatial resampling, geostatistical bootstrap, bootstrap for spatial data	Bayesian spatial analysis, Bayesian geostatistics, spatial Bayesian modeling, Bayesian areal modeling
Связанные	4	2
Сводка	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.