Exact inference in Bayesian networks with nodes having a large parent set is not tractable using standard techniques as are the junction tree method or the variable elimination. However, in many applications, the conditional probability tables of these nodes have certain local structure than can be exploited to make the exact inference tractable. In this paper we combine the CP tensor decomposition of probability tables with probabilistic inference using weighted model counting. The motivation for this combination is to exploit not only the local structure of some conditional probability tables but also other structural information potentialy present in the Baysian network, like determinism or context specific independence. We illustrate the proposed combination on BN2T networks – two-layered Bayesian networks with conditional probability tables representing noisy threshold models.