Salīdzināt metodes

	Beijes kriginga (modelēta ģeostatistika)	Universālā krigēšana (krigēšana ar trendu)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1993–1998	1969
Autors	Diggle, Tawn & Moyeed; Handcock & Stein	Georges Matheron
Tips	Bayesian spatial interpolation	Geostatistical interpolation with spatial trend
Pirmavots	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.
Citi nosaukumi	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
Saistītās	5	3
Kopsavilkums	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.