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Bayesian networks (BNs) are probabilistic graphical models that are widely used for building diagnosis-and decision-support expert systems. The construction of BNs with the help of human experts is a difficult and time consuming task, which is prone to errors and omissions especially when the problems are very complicated. Learning the structure of a Bayesian network model and causal relations from a dataset or database is important for large BNs analysis. This paper focuses on using a SMILEdoi:10.1109/icsmc.2009.5346198 dblp:conf/smc/JongsawatP09a fatcat:q5ncofq5n5crlgajgmh734wrhi