In the science of Bayesian modeling one method involves using Gaussian processes to derive regression functions on data. I notice in looking at the plots for such regressions that they resemble Kriging plots. For example,
This plot shows three random functions drawn from a Bayesian prior conditioned on the five noise free observations shown as crosses. The shaded area is the pointwise mean plus and minus two times the standard deviation for each input value (corresponding to the 95% confidence region), for the prior and posterior respectively.
Are Gaussian process regressions the same thing as (a new name for) Kriging or is there some difference between the methods?