Bandingkan kaedah

	Model Ralat Ruang Bayesian	Regresi Berbobot Geografi (GWR)
Bidang	Analisis Reruang	Analisis Reruang
Keluarga	Regression model	Regression model
Tahun asal	1988 (classical SEM); 2009 (Bayesian formulation)	2002
Pengasas	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)	Fotheringham, Brunsdon & Charlton
Jenis	Bayesian spatial regression	Local spatial regression
Sumber perintis	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Alias	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Berkaitan	6	5
Ringkasan	The Bayesian Spatial Error Model (Bayesian SEM) estimates a regression in which spatially correlated disturbances are explicitly modelled through a spatial weights matrix, while all parameters — regression coefficients, spatial error autocorrelation, and error variance — receive full posterior distributions via Bayesian inference rather than 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.