In my book they have a stochastic process $X(t)$ and then find that:
$\mathbb{E}[(X(t)-x_{0})^{3}]=x_{0}^{3}[e^{3t\alpha^{2}}-3e^{t\alpha^{2}}+2]$
Where $x_{0}$ is a constant and $\mathbb{E}[X(t)]=x_{0}$. They then say this shows that $X(t)$ is not a Gaussian process. Why is that? I am assuming that it has to do with the fact that for a Gaussian process all finite dimensional distributions are Gaussian, but I cant quite see why the result follows?
Thanks!