Linganisha mbinu

	Bayesian Universal Kriging	Ordinary Kriging
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1990s–2000s	1963
Mwanzilishi	Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein	Georges Matheron (formalising D.G. Krige's empirical work)
Aina	Bayesian geostatistical interpolation with trend	Geostatistical interpolation
Chanzo asilia	Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079	Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266.
Majina mbadala	BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging	OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor
Zinazohusiana	6	4
Muhtasari	Bayesian Universal Kriging (BUK) extends classical universal kriging by placing prior distributions on trend coefficients and spatial covariance parameters, then propagating full posterior uncertainty into predictions. It interpolates spatially referenced continuous data while simultaneously estimating large-scale deterministic trends driven by covariates and small-scale stochastic spatial dependence, yielding prediction intervals that honestly account for both parameter and interpolation uncertainty.	Ordinary Kriging (OK) is the standard geostatistical method for interpolating a continuous spatial variable at unsampled locations. It derives optimal, unbiased weights from the spatial covariance structure of the data, making it the Best Linear Unbiased Predictor (BLUP) under stationarity assumptions. Unlike simpler distance-based methods, it also provides a prediction uncertainty (kriging variance) at every interpolated point.