Some Notes on the Factorization of Probabilistic Logical Models under Maximum Entropy Semantics

Probabilistic conditional logics offer a rich and well-founded framework for designing expert systems. The factorization of their maximum entropy models has several interesting applications. In this paper a general factorization is derived providing a more rigorous proof than in previous work. It yields an approach to extend Iterative Scaling variants to deterministic knowledge bases. Subsequently the connection to Markov Random Fields is revisited.