Consistent estimation on finite parameter sets with application to linear systems identification

Abstract

The consistency of maximum likelihood and related Bayesian estimates for a general class of observation sequences is treated, following a result by P. E. Caines. The condition for consistency is then interpreted in terms of the statistics associated with linear systems driven by white Gaussian inputs, to establish a verifiable condition for the identifiability of such systems on finite sets of mathematical representations.