A technique that includes predicting data acquired by a network of sensors based at least in part on a graphical model of the network, where the graphical model includes true value nodes, observed value nodes and edge factors based at least in part on historical pairwise dependencies for the observed value nodes. The technique includes detecting anomalous sensor data based at least in part on the predicted data.