Bayesian Networks (BN) are efficient to represent knowledge and for the reasoning in uncertainty. However the classic BN requires manual definition of the network structure by an expert, who also defines the values entered into the conditional probability tables. In practice, it can be time-consuming, hence the article proposes the use of Learning Bayesian Networks (LBN). The aim of the study is not only to present LBN, which can be helpful in civil engineering problems, but also to analyze and evaluate the potential of a selected software. Based on a real example the functional values of the Open Markov, Hugin and AgenaRisk applications were compared.
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