  Bayesian approaches to learn the graphical structure of Bayesian Belief
Networks (BBNs) from databases share the assumption that the database is
complete, that is, no entry is reported as unknown. Attempts to relax this
assumption involve the use of expensive iterative methods to discriminate among
different structures. This paper introduces a deterministic method to learn the
graphical structure of a BBN from a possibly incomplete database. Experimental
evaluations show a significant robustness of this method and a remarkable
independence of its execution time from the number of missing data.
