Confusion related to difference of kriging and gaussian processes I am having a hard time understanding what is the difference between kriging and gaussian processes. I mean wiki says they are the same but their formulas for prediction are so different. 
I am a bit confused why they are called similar. Clarifications?
 A: There are some subtle differences between ordinary and simple kriging, maybe that confuses you. GP regression in the way it is usually presented is analogous to simple  kriging. In the Gaussian process Wikipedia entry it says that the article refers explicitly to a "zero-meaned distribution"; that is the same assumption found in simple kriging. 
Also generally speaking kriging is usually performed in a 2 or 3 dimensional spaces, (eg. pollutant concentration along some given area) while most GPR toy examples are one dimensional (eg. $CO_2$ concentration in the atmosphere against time). 
Ultimately kriging/GPR is an interpolation technique and most (not all) of the difference among the variants of it lays on the assumption about the mean trend $\mu(X)$ (or E[$X_t$] if you like this notation better).
A: GPs are known as kriging in geostatistics. To learn about the history of Gaussian Processes watch this video http://youtu.be/4r463NLq0jU?t=26s
A: Kriging is a type of Gaussian process that uses a spatial covariance function or kernel.
These are some helpful resources :
https://arxiv.org/pdf/1708.02663.pdf - talks about gradient enhanced Kriging with PLS but the formulas are the same as Kriging.
https://distill.pub/2019/visual-exploration-gaussian-processes/ - explains Gaussian processes and shows effect of various kernels on the covariance matrix.
