Probabilistic inference with noisy-threshold models based on a CP tensor decomposition ?
CP decomposition of CPTs of noisy threshold and exactly l-out-of-k functions. Symmetric rank of these tensors in the real and complex domains. Experiments on subnetworks of the QMR-DT generalized by using noisy-thresholds. Superiority of the CP decomposition to the parent divorcing method.
probabilistic inference bayesian networks candecomp-parafac tensor decomposition symmetric tensor rank