Generating Bayesian Networks from Probability Logic Knowledge Bases
  We present a method for dynamically generating Bayesian networks from
knowledge bases consisting of first-order probability logic sentences. We
present a subset of probability logic sufficient for representing the class of
Bayesian networks with discrete-valued nodes. We impose constraints on the form
of the sentences that guarantee that the knowledge base contains all the
probabilistic information necessary to generate a network. We define the
concept of d-separation for knowledge bases and prove that a knowledge base
with independence conditions defined by d-separation is a complete
specification of a probability distribution. We present a network generation
algorithm that, given an inference problem in the form of a query Q and a set
of evidence E, generates a network to compute P(Q|E). We prove the algorithm to
be correct.
