Finite Sample Analysis for Structured Discrete System Identification

Xiaotian Xie; Dimitrios Katselis; Carolyn L. Beck; R. Srikant

doi:10.1109/TAC.2023.3236243

Finite Sample Analysis for Structured Discrete System Identification

Xiaotian Xie, Dimitrios Katselis, Carolyn L. Beck, R. Srikant

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	6345-6352
Number of pages	8
Journal	IEEE Transactions on Automatic Control
Volume	68
Issue number	10
DOIs	https://doi.org/10.1109/TAC.2023.3236243
State	Published - Oct 1 2023

Keywords

Discrete state-space dynamical systems
Markov chains
identification
sample complexity

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Computer Science Applications

Online availability

10.1109/TAC.2023.3236243

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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.",

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author = "Xiaotian Xie and Dimitrios Katselis and Beck, {Carolyn L.} and R. Srikant",

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AU - Srikant, R.

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AB - 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.

KW - Discrete state-space dynamical systems

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Finite Sample Analysis for Structured Discrete System Identification

Abstract

Keywords

ASJC Scopus subject areas

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