  We introduce and demonstrate two linear inverse modelling methods for systems
of stochastic ODE's with accuracy that is independent of the dimensionality
(number of elements) of the state vector representing the system in question.
Truncation of the state space is not required. Instead we rely on the principle
that perturbations decay with distance or the fact that for many systems, the
state of each data point is only determined at an instant by itself and its
neighbours. We further show that all necessary calculations, as well as
numerical integration of the resulting linear stochastic system, require
computational time and memory proportional to the dimensionality of the state
vector.
