Vertaile menetelmiä

	Bayesiläinen spatiaalisen viiveen malli	Bayesilainen spatiaalinen virhemalli
Tieteenala	Spatiaalianalyysi	Spatiaalianalyysi
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1997	1988 (classical SEM); 2009 (Bayesian formulation)
Kehittäjä	LeSage (1997); fully elaborated in LeSage & Pace (2009)	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)
Tyyppi	Bayesian spatial regression	Bayesian spatial regression
Alkuperäislähde	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
Rinnakkaisnimet	Bayesian SAR model, Bayesian spatial autoregressive model, BSLM, Bayesian SLM	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model
Liittyvät	5	6
Tiivistelmä	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.