Tractable Learning and Inference for Large-Scale Probabilistic Boolean Networks

Probabilistic Boolean Networks (PBNs) have been previously proposed so as to gain insights into complex dynamical systems. However, identification of large networks and of the underlying discrete Markov Chain which describes their temporal evolution, still remains a challenge. In this paper, we introduce an equivalent representation for the PBN, the Stochastic Conjunctive Normal Form (SCNF), which paves the way to a scalable learning algorithm and helps predict longrun dynamic behavior of large-scale systems. Moreover, SCNF allows its efficient sampling so as to statistically infer multistep transition probabilities which can provide knowledge on the activity levels of individual nodes in the long run.