Vertaile menetelmiä

	Bayesiläinen spatiaalinen autokorrelaatio	Bayesiläinen spatiaalinen regressio
Tieteenala	Spatiaalianalyysi	Spatiaalianalyysi
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1991	1990s–2000s
Kehittäjä	Besag, York & Mollie	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors
Tyyppi	Bayesian hierarchical spatial model	Bayesian hierarchical regression
Alkuperäislähde	Besag, J., York, J., & Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1), 1–20.	Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173
Rinnakkaisnimet	Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model
Liittyvät	6	3
Tiivistelmä	Bayesian Spatial Autocorrelation embeds spatial dependence directly into a Bayesian hierarchical model. A Conditional Autoregressive (CAR) prior encodes the expectation that neighboring areas are more similar than distant ones, and posterior inference is obtained via MCMC. This approach is especially valuable in disease mapping, ecology, and regional science, where small-area estimates need borrowing strength across neighbors.	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.