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Distributed information inference in wireless sensor networks is of significant importance for many real-world applications in which graphical modeling of a deployed wireless sensor network is fundamental. One critical issue faced today is how to learn the graphical model parameters of a deployed sensor network as efficiently as possible, since it is usually expensive or even impossible to collect...
Due to its compact and distributed nature, loopy belief propagation has been proved to be theoretically appropriate as a basis to systematically deal with uncertainties with incomplete or corrupted observations in the broad applications of wireless sensor networks. However, the transmissions of massive belief messages of loopy belief propagation could become a serious concern from the energy consumption...
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