Bayesian networks for continuous values and uncertainty in the learning process

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. K e y w o r d s : Bayesian networks, uncertainty, knowledge representation, fuzzy partition.