Salīdzināt metodes

	Beiešiskais Morana I	Bayesian Spatial Regression
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1950 / 2000s	1990s–2000s
Autors	Moran (1950), Bayesian extension developed in spatial statistics literature (late 1990s–2000s)	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors
Tips	Bayesian spatial autocorrelation test	Bayesian hierarchical regression
Pirmavots	Haining, R. (2003). Spatial Data Analysis: Theory and Practice. Cambridge University Press. ISBN: 9780521774611	Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173
Citi nosaukumi	Bayesian spatial autocorrelation test, Bayesian Moran statistic, Moran's I under Bayesian inference, Bayesian global spatial association	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model
Saistītās	6	3
Kopsavilkums	Bayesian Moran's I embeds the classical Moran's I spatial autocorrelation test within a Bayesian probabilistic framework. Rather than producing a single p-value, it yields a posterior distribution over the spatial autocorrelation parameter, enabling uncertainty quantification, incorporation of prior knowledge, and more principled inference in small or irregular spatial datasets.	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.