Abstract. The K2 metric is a well-known evaluation measure (or scoring function) for learning Bayesian networks from data . It is derived by assuming uniform prior distributions on the values of an attribute for each possible instantiation of its parent attributes. This assumption introduces a tendency to select simpler network structures. In this paper we modify the K2 metric in three different ways, introducing a parameter by which the strength of this tendency can be controlled. Our experiments with the ALARM network  and the BOBLO network  suggest that—somewhat contrary to our expectations—a slightly stronger tendency towards simpler structures may lead to even better results.
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