Quickest detection of Markov networks

Heydari, Javad; Tajer, Ali; Poor, H. Vincent

doi:10.1109/isit.2016.7541517

Cited by 10 publications

(6 citation statements)

References 46 publications

(113 reference statements)

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“…This results in open-loop search strategies, which is fundamentally different from the setting in this paper. Other recent studies include searching for correlation structures of Markov networks [55], searching for a moving Markovian target [58], and approximating optimal policies via deep reinforcement learning techniques [59], [60].…”

Section: Other Related Workmentioning

confidence: 99%

Anomaly Search with Multiple Plays under Delay and Switching Costs

Lambez,

Cohen

2021

Preprint

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The problem of searching for L anomalous processes among M processes is considered. At each time, the decision maker can observe a subset of K processes (i.e., multiple plays). The measurement drawn when observing a process follows one of two different distributions, depending on whether the process is normal or abnormal. The goal is to design a policy that minimizes the Bayes risk which balances between the sample complexity, detection errors, and the switching cost associated with switching across processes. We develop a policy, dubbed consecutive controlled sensing (CCS), to achieve this goal. On the one hand, by contrast to existing studies on controlled sensing, the CCS policy senses processes consecutively to reduce the switching cost. On the other hand, the policy controls the sensing operation in a closed-loop manner to switch between processes when necessary to guarantee reliable inference. We prove theoretically that CCS is asymptotically optimal in terms of minimizing the Bayes risk as the detection error approaches zero (i.e., the sample complexity increases). Simulation results demonstrate strong performance of CCS in the finite regime as well.

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Section: Other Related Workmentioning

confidence: 99%

Anomaly Search with Multiple Plays under Delay and Switching Costs

Lambez,

Cohen

2021

Preprint

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“…Other recent studies include searching for a moving Markovian target [46], and searching for correlation structures of Markov networks [47].…”

Section: B Related Workmentioning

confidence: 99%

Searching for Anomalies Over Composite Hypotheses

Hemo

Gafni

Cohen

et al. 2020

IEEE Trans. Signal Process.

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The problem of detecting anomalies in multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a common distribution with an unknown parameter (vector), whose value lies in normal or abnormal parameter spaces, depending on its state. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. We develop a deterministic search algorithm with the following desired properties. First, when no additional side information on the process states is known, the proposed algorithm is asymptotically optimal in terms of minimizing the detection delay as the error probability approaches zero. Second, when the parameter value under the null hypothesis is known and equal for all normal processes, the proposed algorithm is asymptotically optimal as well, with better detection time determined by the true null state. Third, when the parameter value under the null hypothesis is unknown, but is known to be equal for all normal processes, the proposed algorithm is consistent in terms of achieving error probability that decays to zero with the detection delay. Finally, an explicit upper bound on the error probability under the proposed algorithm is established for the Bar Hemo, Tomer Gafni and Kobi Cohen are with the

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“…Chernoff's rule, specifically, at each time, identifies the most likely decision based on the collected data and takes the action that reinforces the decision. Extensions of the Chernoff's rule to various settings are studied in [2][3][4][5] including the more recent studies that are relevant to the scope of this paper in [6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Active Estimation From Multimodal Data

Mukherjee

Tajer

Chen

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The paper considers the problem of estimating a covariate parameter shared by multiple statistical models. Under the objective of estimating the parameter with target reliability with the fewest number of samples from these models, a fundamental question is how to glean samples from the statistical models. This question is especially important when the models are not equally descriptive or informative about the parameter, each being the most informative only for a specific regime of the parameter. This paper provides 1) an active sampling framework that specifies how the samples should be collected from different models over time in a data-adaptive fashion; 2) a stopping criterion specifying when the collected data is informative enough to form a reliable estimate for the covariate parameter; and 3) a terminal estimation rule. These rules, collectively, are shown to admit certain optimality guarantees. Numerical evaluations are provided to compare the performance with relevant existing approaches.

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Quickest detection of Markov networks

Cited by 10 publications

References 46 publications

Anomaly Search with Multiple Plays under Delay and Switching Costs

Anomaly Search with Multiple Plays under Delay and Switching Costs

Searching for Anomalies Over Composite Hypotheses

Active Estimation From Multimodal Data

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