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We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihood estimate of the parameters. This scheme doesn't involve explicit point-to-point message passing or routing; instead, it diffuses information across the network by updating each node's data with adoi:10.1109/ipsn.2005.1440896 dblp:conf/ipsn/XiaoBL05 fatcat:pcgiwoi5uzdatmxl5t2kkwdwjy