  Probabilistic Boolean Networks (PBNs) have been previously proposed so as to
gain insights into complex dy- namical 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 long- run dynamic behavior of large-scale systems. Moreover, SCNF
allows its efficient sampling so as to statistically infer multi- step
transition probabilities which can provide knowledge on the activity levels of
individual nodes in the long run.
