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A well-behaved algorithm for simulating dependence structures of Bayesian networks. (English) Zbl 1171.68765
Summary: Automatic generation of Bayesian network (BN) structures (directed acyclic graphs) is an important step in experimental study of algorithms for inference in BNs and algorithms for learning BNs from data. Previously known simulation algorithms do not guarantee connectedness of generated structures or even successful generation according to a user specification. We propose a simple, efficient and well-behaved algorithm for automatic generation of BN structures. The performance of the algorithm is demonstrated experimentally.

MSC:
68T37 Reasoning under uncertainty in the context of artificial intelligence
68R10 Graph theory (including graph drawing) in computer science
Software:
WEBWEAVR-III
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