A well-behaved algorithm for simulating dependence structures of Bayesian networks

Automatic generation of Bayesian network (BNs) structures (directed acyclic graphs) is an important step in experimental study of algorithms for inference in BNs and algorithms for learning BNs from data. Pre-viously known simulation algorithms do not guarantee connectedness of generated structures or even successful genearation according to a user specification. We propose a simple, efficient and well-behaved algorithm for automatic generation of BN structures. The performance of the algo-rithm is demonstrated experimentally