Compară metode

	Indicatori bayesieni locali de asociere spațială (Bayesian LISA)	Autocorelarea Spațială Bayesiană
Domeniu	Analiză spațială	Analiză spațială
Familie	Regression model	Regression model
Anul apariției	2000s–2010s	1991
Autorul original	Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand)	Besag, York & Mollie
Tip	Bayesian local spatial statistic	Bayesian hierarchical spatial model
Sursa seminală	Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.	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.
Denumiri alternative	Bayesian LISA, Bayesian local spatial autocorrelation, Bayesian local Moran, B-LISA	Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA
Înrudite	6	6
Rezumat	Bayesian Local Indicators of Spatial Association extend the classical LISA framework by embedding local spatial association statistics within a Bayesian hierarchical model. Rather than relying on asymptotic permutation-based significance tests, this approach places prior distributions on spatial parameters and derives posterior probabilities that a location is part of a genuine spatial cluster, accounting for uncertainty and borrowing strength across nearby units.	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.