Finite Sample Analysis for Structured Discrete System Identification

doi:10.1109/TAC.2023.3236243

Finite Sample Analysis for Structured Discrete System Identification

2023-10-01

1

PRE

AI

DOI：10.1109/TAC.2023.3236243

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Discrete state-space dynamical systems

identification

Markov chains

sample complexity

Finite Sample Analysis for Structured Discrete System Identification

Abstract

Journal

Organization