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Abstract: Graphical models have been widely applied in solving distributed inferenceproblems in sensor networks. In this paper, the problem of coordinating anetwork of sensors to train a unique ensemble estimator under communicationconstraints is discussed. The information structure of graphical models withspecific potential functions is employed, and this thus converts thecollaborative training task into a problem of local training plus globalinference. Two important classes of algorithms of graphical model inference,message-passing algorithm and sampling algorithm, are employed to tacklelow-dimensional, parametrized and high-dimensional, non-parametrized problemsrespectively. The efficacy of this approach is demonstrated by concreteexamples.



Author: Haipeng Zheng, Sanjeev R. Kulkarni, H. Vincent Poor

Source: https://arxiv.org/







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