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This paper considers the problem of distributed inferencing in a sensor network. It particularly explores the probabilistic inferencing problem in the context of a distributed Boltzmann machine-based framework for monitoring the network. The paper offers a variational mean-field approach to develop communication-efficient local algorithm for Variational Inferencing in Distributed Environments (VIDE). It compares the performance of the proposed approximate variational technique with respect todoi:10.1016/j.jpdc.2007.07.011 fatcat:ajajzhmplfdzfnxf54amrqpabq