Porównaj metody

	Bayesian Geographically Weighted Regression (BGWR)	Bayesowska regresja przestrzenna
Dziedzina	Analiza przestrzenna	Analiza przestrzenna
Rodzina	Regression model	Regression model
Rok powstania	2007	1990s–2000s
Twórca	Wheeler & Calder (2007); Finley (2011)	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors
Typ	Bayesian spatially varying coefficient regression	Bayesian hierarchical regression
Źródło pierwotne	Finley, A. O. (2011). Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods in Ecology and Evolution, 2(2), 143-154.	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	BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model
Pokrewne	5	3
Podsumowanie	Bayesian Geographically Weighted Regression combines the spatially varying coefficient framework of GWR with Bayesian inference, placing Gaussian process priors on the locally varying regression coefficients. This yields full posterior distributions over each coefficient at every location, providing principled uncertainty quantification rather than only point estimates.	Bayesian Spatial Regression embeds a spatially structured random effect into a regression framework and estimates all parameters — including spatial range and variance — through posterior inference rather than point estimation. It handles spatial autocorrelation, quantifies full predictive uncertainty, and accommodates small or irregular spatial datasets via hierarchical priors.