Normal Factor Graphs as Probabilistic Models

Abstract

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.

References in corpus (5)

• Normal Factor Graphs and Holographic Transformations
• Extending Factor Graphs so as to Unify Directed and Undirected Graphical Models
• Cumulative distribution networks and the derivative-sum-product algorithm
• Inference with Multivariate Heavy-Tails in Linear Models
• Convolutional Factor Graphs as Probabilistic Models