On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Sensor management using an active sensing approach
Signal Processing
Epidemic thresholds in real networks
ACM Transactions on Information and System Security (TISSEC)
Foundations and Applications of Sensor Management
Foundations and Applications of Sensor Management
Partially Observable Markov Decision Process Approximations for Adaptive Sensing
Discrete Event Dynamic Systems
Factored particles for scalable monitoring
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Adaptive diagnosis in distributed systems
IEEE Transactions on Neural Networks
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Adaptively monitoring the states of nodes in a large complex network is of interest in domains such as national security, public health, and energy grid management. Here, we present an information theoretic adaptive tracking and sampling framework that recursively selects measurements using the feedback from performing inference on a dynamic Bayesian Network. We also present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of hidden node states resulting from actively monitoring the network. Since traditional epidemic thresholds are derived using observation independent Markov chains, the threshold of the posterior should more accurately model the true phase transition of a network. The adaptive tracking framework and epidemic threshold should provide insight into modeling the dynamic response of the updated posterior to active intervention and control policies while monitoring modern complex networks.