Linganisha mbinu

	Uhuru wa Angani wa Kibayesiyani (Bayesian Spatial Autocorrelation)	Kriging ya Bayesian (Jiostatistiki Zinazotegemea Model)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1991	1993–1998
Mwanzilishi	Besag, York & Mollie	Diggle, Tawn & Moyeed; Handcock & Stein
Aina	Bayesian hierarchical spatial model	Bayesian spatial interpolation
Chanzo asilia	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.	Diggle, P. J., Tawn, J. A., & Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(3), 299–350.
Majina mbadala	Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging
Zinazohusiana	6	5
Muhtasari	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 Kriging embeds classical geostatistical interpolation inside a full probabilistic framework. Instead of treating variogram parameters as fixed point estimates, it places prior distributions on them and updates these priors with observed spatial data to obtain a posterior distribution. Predictions at unsampled locations are then marginalised over this uncertainty, yielding honest predictive intervals that account for both spatial dependence and parameter uncertainty.