Comparer des méthodes

	Régression Bayésienne Pondérée Géographiquement (BGWR)	Régression Pondérée Géographiquement (GWR)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2007	2002
Auteur d'origine	Wheeler & Calder (2007); Finley (2011)	Fotheringham, Brunsdon & Charlton
Type	Bayesian spatially varying coefficient regression	Local spatial regression
Source fondatrice	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.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Alias	BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Apparentées	5	5
Résumé	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.	Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.