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	Model Bayesański z Lagiem Przestrzennym	Autokorelacja przestrzenna
Dziedzina	Analiza przestrzenna	Analiza przestrzenna
Rodzina	Regression model	Regression model
Rok powstania	1997	1950
Twórca	LeSage (1997); fully elaborated in LeSage & Pace (2009)	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Typ	Bayesian spatial regression	Spatial statistic / exploratory spatial data analysis
Źródło pierwotne	LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Inne nazwy	Bayesian SAR model, Bayesian spatial autoregressive model, BSLM, Bayesian SLM	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Pokrewne	5	5
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.	Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations.