Data generation for testing DAG-structured Bayesian networks

In this paper we have solved the open problem of generating random vectors when the underlying structure obeyed by the dependence graph is a Directed Acyclic Graph (DAG). To the best of our knowledge, our work is of a pioneering sort. We present a formal strategy for the case when the DAG structure and the marginals are given. The paper presents the formal algorithm, proves its correctness, derives its complexity, and presents examples for both artificial data, and for date that is intended to artificially populate a medical database. The method has also been used for testing the ALARM network.