Linganisha mbinu

	Kriging ya Bayesian (Jiostatistiki Zinazotegemea Model)	Ukridingi wa Ulimwengu (Ukridingi wenye Mwenendo)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1993–1998	1969
Mwanzilishi	Diggle, Tawn & Moyeed; Handcock & Stein	Georges Matheron
Aina	Bayesian spatial interpolation	Geostatistical interpolation with spatial trend
Chanzo asilia	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.	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Majina mbadala	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
Zinazohusiana	5	3
Muhtasari	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.	Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling with latitude or a pollutant gradient, while still returning prediction variances.