Abstract
Probabilistic relational models (PRMs) were introduced to extend the modelling and reasoning capacities of Bayesian networks from propositional to relational domains. PRMs are typically learned from relational data, by extracting from these data both a dependency structure and its numerical parameters. For this purpose, a large and rich data set
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