Suppose a function $G:\mathbb{R}^d\rightarrow\mathbb{R}$ is given.


What are some necessary or sufficient conditions on $G$ for there to exist a probability space $(\Omega,\mathcal{F},\mathbb{P})$ and a jointly-measurable function $V:\Omega\times\mathbb{R}^d\rightarrow \mathbb{R}$ that realizes a spatially homogenous Gaussian random field with $\mathbb{E}(V(\cdot,x)) = 0$ and $\mathbb{E}(V(\cdot,x)V(\cdot,y)) = G(x-y)$?



Existence of Gaussian random field with prescribed covariance
The necessary and sufficient condition is for the function $G$ to be positive definite, that is, for the matrix $(G(x\_j-x\_k))\_{j,k\in[n]}$ to be positive definite for any natural $n$ and any distinct $x\_1,\dots,x\_n$ in $\mathbb R^d$, where $[n]:=\{1,\dots,n\}$.


