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Learning hybrid Bayesian networks from data
, 1998
"... We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first method ..."
Abstract

Cited by 11 (1 self)
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We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first methodology uses discretized data to learn the Bayesian network structure, and the original nondiscretized data for the parameterization of the learned structure. The second methodology uses nondiscretized data both to learn the Bayesian network structure and its parameterization. For the direct handling of continuous data, we propose the use of artificial neural networks as probability estimators, to be used as an integral part of the scoring metric defined to search the space of Bayesian network structures. With both methodologies, we assume the availability of a complete dataset, with no missing values or hidden variables. We report experimental results aimed at comparing the two methodologies. These results provide evidence that learning with discretized data presents advantages both in terms of efficiency and in terms of accuracy of the learned models over the alternative approach of using nondiscretized data.
Learning Bayesian belief networks with neural network estimators
 In Neural Information Processing Systems 9
, 1997
"... 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 participat ..."
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Cited by 6 (2 self)
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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...