Linganisha mbinu

	Uchambuzi wa Kipeo cha Moto cha Kibayeshi	Uhuru wa Angani wa Kibayesiyani (Bayesian Spatial Autocorrelation)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1987	1991
Mwanzilishi	Clayton & Kaldor (1987); Lawson (2001 onward)	Besag, York & Mollie
Aina	Bayesian spatial cluster detection	Bayesian hierarchical spatial model
Chanzo asilia	Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (3rd ed.). CRC Press. ISBN: 978-1138575424	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.
Majina mbadala	Bayesian spatial cluster detection, Bayesian disease mapping hot spots, empirical Bayesian hot spot analysis, Bayesian spatial smoothing hot spots	Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA
Zinazohusiana	5	6
Muhtasari	Bayesian Hot Spot Analysis identifies spatial clusters of elevated risk or intensity by combining observed data with prior beliefs about spatial structure. It uses Bayesian smoothing — pooling information across neighboring areas — to stabilize estimates in small areas and then flags locations where the posterior probability of exceeding a risk threshold is high.	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.