Structure Refinement in First Order Conditional Influence Language

In this paper, we present preliminary results from learning the structure of first-order conditional influence statements from data for the purpose of classification. In order to reduce the search space over structures, we formulate and address the structure learning problem as a problem of refining the structure of a first-order probabilistic program using training data. We use variants of the conditional BIC scoring metric to refine the program to best fit the data. We use a previously introduced language called FOCIL which consists of statements that can be instantiated and composed into a propositional Bayesian network. The results on a synthetic dataset and a real-world task show that the algorithm achieves error rates comparable to the gold standard program with a reasonable amount of training data.