Salīdzināt metodes

	Bayesian Spatial Autocorrelation	Beijes kriginga (modelēta ģeostatistika)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1991	1993–1998
Autors	Besag, York & Mollie	Diggle, Tawn & Moyeed; Handcock & Stein
Tips	Bayesian hierarchical spatial model	Bayesian spatial interpolation
Pirmavots	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.
Citi nosaukumi	Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging
Saistītās	6	5
Kopsavilkums	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.