TY - GEN
T1 - On Concentration Bounds for Bayesian Identification of Linear Non-Gaussian Systems
AU - Kim, Yeoneung
AU - Kim, Gihun
AU - Yang, Insoon
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We adopt a Bayesian perspective to identify the unknown parameters of linear stochastic systems with possibly non-Gaussian disturbance distributions. The key idea of our algorithm is to alternately execute L randomly selected linear state-feedback controllers and keep track of a maximum a posteriori estimator. The proposed algorithm asymptotically achieves the concentration of posterior distributions around the true system parameters. We also derive probabilistic bounds for the concentration based on the classical results regarding the asymptotic properties of posterior distributions. An empirical demonstration is provided as well.
AB - We adopt a Bayesian perspective to identify the unknown parameters of linear stochastic systems with possibly non-Gaussian disturbance distributions. The key idea of our algorithm is to alternately execute L randomly selected linear state-feedback controllers and keep track of a maximum a posteriori estimator. The proposed algorithm asymptotically achieves the concentration of posterior distributions around the true system parameters. We also derive probabilistic bounds for the concentration based on the classical results regarding the asymptotic properties of posterior distributions. An empirical demonstration is provided as well.
UR - https://www.scopus.com/pages/publications/85184803915
U2 - 10.1109/CDC49753.2023.10384233
DO - 10.1109/CDC49753.2023.10384233
M3 - Conference contribution
AN - SCOPUS:85184803915
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 3506
EP - 3511
BT - 2023 62nd IEEE Conference on Decision and Control, CDC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 62nd IEEE Conference on Decision and Control, CDC 2023
Y2 - 13 December 2023 through 15 December 2023
ER -