Convergence results for relational Bayesian networks
Manfred Jaeger · 2002
Relational Bayesian networks are an extension of the method of probabilistic model construction by Bayesian networks. They define probability distributions on finite relational structures by conditioning the probability of a ground atom r(a/sub 1/, ..., a/sub n/) on first-order properties of a/sub 1/, ..., a/sub n/ that have been established by previous random decisions. In this paper we investigate from a finite model theory perspective the convergence properties of the distributions defined in this manner. A subclass of relational Bayesian networks is identified that define distributions with convergence laws for first-order properties.