TY - GEN
T1 - Toward Uncertainty Aware Quickest Change Detection
AU - Hare, James Zachary
AU - Kaplan, Lance
AU - Veeravalli, Venugopal V.
N1 - Funding Information:
ACKNOWLEDGEMENT This work was supported by the CCDC Army Research Laboratory under Cooperative Agreement W911NF-17-2-0196 (ARL IoBT CRA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2021 International Society of Information Fusion (ISIF).
PY - 2021
Y1 - 2021
N2 - We study the problem of Quickest Change Detection (QCD) where the parameters of both the pre- and post-change distributions are completely unknown or known within a second-order distribution generated from training data. We propose the use of the Uncertain Likelihood Ratio (ULR) test statistic, which is designed from a Bayesian perspective in contrast with the traditional frequentist approach, i.e., the Generalized Likelihood Ratio (GLR) test. The ULR test utilizes a ratio of posterior predictive distributions, which incorporates parameter uncertainty into the likelihood estimates when there is a lack of or limited availability of training samples. Through an empirical study, we show that the proposed test outperforms the GLR test, while achieving similar results as the classical CUSUM algorithm as the number of training samples goes to infinity.
AB - We study the problem of Quickest Change Detection (QCD) where the parameters of both the pre- and post-change distributions are completely unknown or known within a second-order distribution generated from training data. We propose the use of the Uncertain Likelihood Ratio (ULR) test statistic, which is designed from a Bayesian perspective in contrast with the traditional frequentist approach, i.e., the Generalized Likelihood Ratio (GLR) test. The ULR test utilizes a ratio of posterior predictive distributions, which incorporates parameter uncertainty into the likelihood estimates when there is a lack of or limited availability of training samples. Through an empirical study, we show that the proposed test outperforms the GLR test, while achieving similar results as the classical CUSUM algorithm as the number of training samples goes to infinity.
KW - Limited Training Data
KW - Quickest Change Detection
KW - Uncertain Likelihood Ratio
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M3 - Conference contribution
AN - SCOPUS:85123451571
T3 - Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021
BT - Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on Information Fusion, FUSION 2021
Y2 - 1 November 2021 through 4 November 2021
ER -