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This paper is concerned with a development of a theory on probabilistic models, and in particular Bayesian networks, when handling continuous variables. While it is possible to deal with continuous variables without discretisation, the simplest approach is to discretise them. A fuzzy partition of continuous domains will be used, which requires an inference… (More)
This paper proposes a method for Bayesian networks that handles uncertainty and discretization of continuous variables when learning the networks from a database of cases. The database is reorganised in a new form of representation called reduced database where data are treated as distributions on symbolic values.