Bandingkan metode

	Kriging Universal Bayesian	Kriging Biasa
Bidang	Analisis Spasial	Analisis Spasial
Keluarga	Regression model	Regression model
Tahun asal	1990s–2000s	1963
Pencetus	Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein	Georges Matheron (formalising D.G. Krige's empirical work)
Tipe	Bayesian geostatistical interpolation with trend	Geostatistical interpolation
Sumber perintis	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.
Alias	BUK, Bayesian kriging with trend, Bayesian spatial interpolation with covariates, stochastic universal kriging	OK, kriging interpolation, geostatistical interpolation, BLUE spatial predictor
Terkait	6	4
Ringkasan	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.