方法对比

	Bayesian Spatial Regression	空间误差模型 (SEM)
领域	空间分析	空间分析
方法族	Regression model	Regression model
起源年份	1990s–2000s	1988
提出者	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors	Anselin
类型	Bayesian hierarchical regression	Spatial regression (spatially autocorrelated errors)
开创性文献	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.
别名	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)
相关	3	5
摘要	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.