方法对比

	贝叶斯克里金法（基于模型的地质统计学）	通用克里金 (带趋势的克里金)
领域	空间分析	空间分析
方法族	Regression model	Regression model
起源年份	1993–1998	1969
提出者	Diggle, Tawn & Moyeed; Handcock & Stein	Georges Matheron
类型	Bayesian spatial interpolation	Geostatistical interpolation with spatial trend
开创性文献	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.
别名	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging	kriging with a trend, kriging with drift, trend kriging, evrensel kriging
相关	5	3
摘要	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.