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	Model Bayesański z Lagiem Przestrzennym	Bayesowski Model Błędu Przestrzennego
Dziedzina	Analiza przestrzenna	Analiza przestrzenna
Rodzina	Regression model	Regression model
Rok powstania	1997	1988 (classical SEM); 2009 (Bayesian formulation)
Twórca	LeSage (1997); fully elaborated in LeSage & Pace (2009)	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)
Typ	Bayesian spatial regression	Bayesian spatial regression
Źródło pierwotne	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247
Inne nazwy	Bayesian SAR model, Bayesian spatial autoregressive model, BSLM, Bayesian SLM	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model
Pokrewne	5	6
Podsumowanie	The Bayesian Spatial Lag Model (BSLM) extends the classical spatial autoregressive (SAR) regression by placing prior distributions over all parameters and recovering full posterior distributions via MCMC sampling. It explicitly accounts for spatial dependence — the outcome in one location is partly driven by outcomes in neighboring locations — and yields uncertainty-quantified estimates of both regression coefficients and the spatial autocorrelation parameter rho.	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.