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	Symulacja przestrzenna metodą Monte Carlo	Przestrzenne wnioskowanie bayesowskie
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	1970s–1980s	1991
Twórca	B. D. Ripley and the spatial statistics tradition	Besag, York & Mollie (CAR prior, 1991); Gelfand & colleagues (Bayesian geostatistics, 1990s)
Typ	computational simulation	Bayesian hierarchical spatial model
Źródło pierwotne	Ripley, B. D. (1987). Stochastic Simulation. John Wiley & Sons. ISBN: 978-0471818847	Banerjee, S., Carlin, B. P. & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173
Inne nazwy	spatial MC simulation, Monte Carlo spatial analysis, stochastic spatial simulation, spatial stochastic simulation	Bayesian spatial analysis, Bayesian geostatistics, spatial Bayesian modeling, Bayesian areal modeling
Pokrewne	4	2
Podsumowanie	Spatial Monte Carlo simulation applies random sampling methods to spatial problems, generating many stochastic realisations of a spatial process — such as a random field, point pattern, or network — to estimate distributional properties, propagate uncertainty, or test spatial hypotheses. It is a cornerstone technique in geostatistics, spatial epidemiology, ecology, and environmental modelling.	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.