Finite Sample Analysis for Structured Discrete System Identification

Abstract

We consider a discrete-time dynamical system over a discrete state-space, which evolves according to a structured Markov model called Bernoulli Autoregressive (BAR) model. Our goal is to obtain sample complexity bounds for the problem of estimating the parameters of this model using an indirect Maximum Likelihood Estimator. Our sample complexity bounds exploit the structure of the BAR model and are established using concentration inequalities for random matrices and Lipschitz functions.