Abstract

In this paper we propose a method for learning Bayesian belief networks from data. The method uses artificial neural networks as probability estimators, thus avoiding the need for making prior assumptions on the nature of the probability distributions governing the relationships among the participating variables. This new method has the potential for being applied to domains containing both discrete and continuous variables arbitrarily distributed. We compare the learning performance of this new method with the performance of the method proposed by Cooper and Herskovits in [10]. The experimental results show that, although the learning scheme based on the use of ANN estimators is slower, the learning accuracy of the two methods is comparable. y To appear in Advances in Neural Information Processing Systems, 1996. 1 Introduction Bayesian belief networks (BBN), often referred to as probabilistic networks, are a powerful formalism for representing and reasoning under uncertainty. This...

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