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	Bayesowski Model Błędu Przestrzennego	Model błędu przestrzennego (SEM)
Dziedzina	Analiza przestrzenna	Analiza przestrzenna
Rodzina	Regression model	Regression model
Rok powstania	1988 (classical SEM); 2009 (Bayesian formulation)	1988
Twórca	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)	Anselin
Typ	Bayesian spatial regression	Spatial regression (spatially autocorrelated errors)
Źródło pierwotne	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Inne nazwy	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Pokrewne	6	5
Podsumowanie	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.	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.