  We consider multi-dimensional Gaussian processes and give a new condition on
the covariance, simple and sharp, for the existence of stochastic area(s).
Gaussian rough paths are constructed with a variety of weak and strong
approximation results. Together with a new RKHS embedding, we obtain a powerful
- yet conceptually simple - framework in which to analysize differential
equations driven by Gaussian signals in the rough paths sense.
