Regression modelGIS / spatial

Bayesian Co-Kriging

Bayesian Co-Kriging is a multivariate geostatistical method that uses auxiliary spatially correlated variables to improve predictions of a primary variable of interest. By placing Bayesian priors on cross-covariance parameters, it propagates all uncertainty — including parameter uncertainty — into the prediction intervals, yielding fully probabilistic maps with calibrated uncertainty bounds.

MethodMind'de açSoonVideoSoon

Tam yöntemi oku

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer. ISBN: 978-0387329079
  2. Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173

Related methods

Referenced by

ScholarGateBayesian Co-Kriging (Bayesian Co-Kriging Spatial Interpolation). Retrieved 2026-06-04 from https://scholargate.app/tr/spatial-analysis/bayesian-co-kriging