On the multivariable approximate stochastic realization problem

We consider the problem of giving an approximate Markovian representation for multivariate Gaussian processes. The result is a generalization of the Lindquist-Picci algorithm to derive a minimal stochastic realization from a nonminimal one. The properties of the invariant subspaces of a state space are exploited to produce an approximate model