Linganisha mbinu

	Kriging ya Kawaida ya Kibayesia	Kriging ya Bayesian (Jiostatistiki Zinazotegemea Model)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1993	1993–1998
Mwanzilishi	Handcock & Stein (1993); Diggle & Ribeiro (2007)	Diggle, Tawn & Moyeed; Handcock & Stein
Aina	Bayesian geostatistical interpolation	Bayesian spatial interpolation
Chanzo asilia	Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079	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.
Majina mbadala	Bayesian kriging, BOK, geostatistical Bayesian interpolation, Bayesian spatial prediction	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging
Zinazohusiana	5	5
Muhtasari	Bayesian Ordinary Kriging is a geostatistical interpolation method that combines classical ordinary kriging with a Bayesian framework to jointly estimate the spatial covariance parameters and produce predictions at unsampled locations. Unlike plug-in kriging, it propagates uncertainty about variogram parameters through to the predictive distribution, yielding more honest uncertainty quantification.	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.