  Model estimates obtained from traditional subspace identification methods may
be subject to significant variance. This elevated variance is aggravated in the
cases of large models or of a limited sample size. Common solutions to reduce
the effect of variance are regularized estimators, shrinkage estimators and
Bayesian estimation. In the current work we investigate the latter two
solutions, which have not yet been applied to subspace identification. Our
experimental results show that our proposed estimators may reduce the
estimation risk up to $40\%$ of that of traditional subspace methods.
