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The problem of quickest data-adaptive and sequential search for clusters in a Gauss-Markov random field is considered. In the existing literature, such search for clusters is often performed using fixed sample size and non-adaptive strategies. In order to accommodate large networks, in which data adaptivity leads to significant gains in detection quality(More)
Consider a set of random sequences, each consisting of independent and identically distributed random variables drawn from one of the two known distributions F<sub>0</sub> and F<sub>1</sub>. The underlying distributions of different sequences are correlated, induced by an inherent physical coupling in the mechanisms generating these sequences. The objective(More)
Detecting correlation structures in large networks arises in many domains. Such detection problems are often studied independently of the underlying data acquisition process, rendering settings in which data acquisition policies and the associated sample size are pre-specified. Motivated by the advantages of data-adaptive sampling in data dimensionality(More)
Line outage detection and localization play pivotal roles in contingency analysis, power flow optimization, and situational awareness delivery in power grids. Hence, agile detection and localization of line outages enhance the efficiency of operations and their resilience against cascading failures. This paper proposes a stochastic graphical framework for(More)
Agile localization of anomalous events plays a pivotal role in enhancing the overall reliability of the grid and avoiding cascading failures. This is especially of paramount significance in the large-scale grids due to their geographical expansions and the large volume of data generated. This paper proposes a stochastic graphical framework, by leveraging(More)
A large network of agents in which each agent generates one random variable is considered. The random variables generated by an unknown subset of nodes form a correlation structure, while the remaining nodes generate independent and identically distributed random variables. This paper formalizes and analyzes the quickest search strategy, the goal of which(More)
An ordered set of data sequences is given where, broadly, the data sequences are categorized into normal and abnormal ones. The normal sequences consist of random variables generated according to a known distribution, while there exist uncertainties about the distributions of the abnormal sequences. Moreover, the generations of different sequences are(More)
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