  We present a new probabilistic modelling framework based on the recent notion
of normal factor graph (NFG). We show that the proposed NFG models and their
transformations unify some existing models such as factor graphs, convolutional
factor graphs, and cumulative distribution networks. The two subclasses of the
NFG models, namely the constrained and generative models, exhibit a duality in
their dependence structure. Transformation of NFG models further extends the
power of this modelling framework. We point out the well-known NFG
representations of parity and generator realizations of a linear code as
generative and constrained models, and comment on a more prevailing duality in
this context. Finally, we address the algorithmic aspect of computing the
exterior function of NFGs and the inference problem on NFGs.
