Empirical Bayesian kriging differs from regular kriging in that it calculates the variogram automatically, accounts for uncertainty in the variogram's parameters, and can handle non-stationary data by modeling local variograms.
The differences between empirical Bayesian kriging and regular kriging are essential in understanding spatial interpolation. Firstly, empirical Bayesian kriging automatically calculates the variogram, a critical function describing spatial relationships, instead of requiring a pre-defined model, which is the case with regular kriging.
This leads to the second difference: empirical Bayesian kriging accounts for the uncertainty in the variogram's parameters, while regular kriging assumes that these parameters are known exactly. Thirdly, empirical Bayesian kriging can handle non-stationary data by internally modeling a local rather than a global variogram, which provides flexibility in adjusting to the spatial characteristics of the regional data, unlike regular kriging that assumes stationarity.