Bandingkan metode

	Model Galat Spasial Bayesian	Model Durbin Spasial Bayesian
Bidang	Analisis Spasial	Analisis Spasial
Keluarga	Regression model	Regression model
Tahun asal	1988 (classical SEM); 2009 (Bayesian formulation)	2009
Pencetus	LeSage & Pace (Bayesian treatment); Anselin (classical SEM)	LeSage & Pace
Tipe	Bayesian spatial regression	Bayesian spatial regression
Sumber perintis	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
Alias	Bayesian SEM, Bayesian spatial-error regression, BSEM spatial econometrics, Bayesian spatially correlated error model	Bayesian SDM, Bayesian spatial lag-X model, Bayesian SDM with spatially lagged covariates, BSDM
Terkait	6	6
Ringkasan	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 Bayesian Spatial Durbin Model (BSDM) estimates a spatial regression that simultaneously includes a spatially lagged outcome variable and spatially lagged covariates, using Bayesian inference with Markov Chain Monte Carlo sampling. It captures both endogenous and exogenous spatial spillovers while providing full posterior distributions for all parameters, quantifying uncertainty beyond what classical maximum-likelihood estimation offers.