  We introduce a general method, based on a mapping onto quantum mechanics, for
investigating the large-T limit of the distribution P(r,T) of the nonlinear
functional r[V] = (1/T)\int_0^T dT' V[X(T')], where V(X) is an arbitrary
function of the stationary Gaussian Markov process X(T). For T tending to
infinity at fixed r we find that P(r,T) behaves as exp[-theta(r) T], where
theta(r) is a large deviation function. We present explicit results for a
number of special cases, including the case V(X) = X \theta(X) which is related
to the cooling and the heating degree days relevant to weather derivatives.
