Comparar métodos

	Regresión Espacial Bayesiana	Modelo de Error Espacial (SEM)
Campo	Análisis espacial	Análisis espacial
Familia	Regression model	Regression model
Año de origen	1990s–2000s	1988
Autor original	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors	Anselin
Tipo	Bayesian hierarchical regression	Spatial regression (spatially autocorrelated errors)
Fuente seminal	Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Alias	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Relacionados	3	5
Resumen	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.	The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move together, and it is estimated by maximum likelihood or GMM rather than ordinary least squares.