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BAYESIAN NETWORK STRUCTURAL LEARNING AND INCOMPLETE DATA

Bayesian networks formalism is becoming increasingly popular in a lot of areas such as decision aid, diagnosis and complex systems control, in particular thanks to its inference capabilities, even when data are incomplete. Besides, estimating the parameters of a fixed-structure Bayesian network is easy. However, very few methods are capable of using incomplete cases as a base to determine the structure of a Bayesian network. In this paper, we take up the structural EM algorithm principle [8, 9] and put forward some potential improvements based upon principles recently developed in structural learning with complete data.