Compară metode

	Regresia spațială bayesiană	Modelul de decalaj spațial (SAR / Autoregresiv spațial)
Domeniu	Analiză spațială	Analiză spațială
Familie	Regression model	Regression model
Anul apariției	1990s–2000s	1988
Autorul original	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors	Anselin (textbook formalisation); LeSage & Pace
Tip	Bayesian hierarchical regression	Spatial autoregressive regression
Sursa seminală	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.
Denumiri alternative	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Înrudite	3	5
Rezumat	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 Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.