Bandingkan kaedah

	Kriging Bayesian (Geostatistik Berasaskan Model)	Regresi Spatial Bayesian
Bidang	Analisis Reruang	Analisis Reruang
Keluarga	Regression model	Regression model
Tahun asal	1993–1998	1990s–2000s
Pengasas	Diggle, Tawn & Moyeed; Handcock & Stein	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors
Jenis	Bayesian spatial interpolation	Bayesian hierarchical regression
Sumber perintis	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.	Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173
Alias	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model
Berkaitan	5	3
Ringkasan	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.	Bayesian Spatial Regression embeds a spatially structured random effect into a regression framework and estimates all parameters — including spatial range and variance — through posterior inference rather than point estimation. It handles spatial autocorrelation, quantifies full predictive uncertainty, and accommodates small or irregular spatial datasets via hierarchical priors.