Covariance matrix for Gaussian Process and Wishart distribution I'm reading through this paper on Generalised Wishart Processes (GWP). The paper calculates the covariances between different random variables (following Gaussian Process) using squared exponential covariance function, i.e., $K(x,x') = \exp\left(-\frac{|(x-x')|^2}{2l^2}\right)$. It then says that this covariance matrix follows GWP.
I used to think that a covariance matrix computed from linear covariance function ($K(x,x') = x^Tx'$), follows Wishart Distribution with appropriate parameters. 
My question is, how can we still assume the covariance to follow a Wishart distribution with squared exponential covariance function? Also, in general, what is the necessary condition for a covariance function to produce a Wishart distributed covariance matrix?
 A: What is mixed up is the covariance specification in terms of the ambient space on which the Gaussian process is defined, and the operation that transforms a finite dimensional Gaussian random variable to yield a Wishart distribution. 
If $\mathbf{X} \sim \mathcal{N}(0, \Sigma)$ is a $p$-dimensional Gaussian random variable (a column vector)
with mean 0 and covariance matrix $\Sigma$, the distribution of $\mathbf{W} = \mathbf{X} \mathbf{X}^T$ is a Wishart distribution $W_p(\Sigma, 1)$. Note that $\mathbf{W}$ is a $p \times p$ matrix. This is a general result about how the  quadratic form 
$$\mathbf{x} \mapsto \mathbf{x} \mathbf{x}^T$$
transforms a Gaussian distribution to a Wishart distribution. It holds for any choice of positive definite covariance matrix $\Sigma$. If you have i.i.d. observations $\mathbf{X}_1, \ldots, \mathbf{X}_n$ then with $\mathbf{W}_i = \mathbf{X}_i \mathbf{X}_i^T$ the distribution of 
$$\mathbf{W}_{1} + \ldots + \mathbf{W}_n$$
is a Wishart $W_p(\Sigma, n)$-distribution. Dividing by $n$ we get the empirical covariance matrix $-$ an estimate of $\Sigma$. 
For Gaussian processes there is an ambient space, lets say for illustration that it is $\mathbb{R}$, such that the random variables considered are indexed by elements in the ambient space. That is, we consider a process $(X(x))_{x \in \mathbb{R}}$. It is Gaussian (and for simplicity, here with mean 0) if its finite dimensional marginal distributions are Gaussian, that is, if 
$$\mathbf{X}(x_1, \ldots, x_p) := (X(x_1), \ldots, X(x_p))^T \sim \mathcal{N}(0, \Sigma(x_1, \ldots, x_p))$$
for all $x_1, \ldots, x_p \in \mathbb{R}$. The choice of covariance function, as mentioned by the OP, determines the covariance matrix, that is,
$$\text{cov}(X(x_i), X(x_j)) = \Sigma(x_1, \ldots, x_p)_{i,j} = K(x_i, x_j).$$
Disregarding the choice of $K$ the distribution of 
$$\mathbf{X}(x_1, \ldots, x_p) \mathbf{X}(x_1, \ldots, x_p)^T$$
will be a Wishart $W_p(\Sigma(x_1, \ldots, x_p), 1)$-distribution.
