In this paper we introduce an algorithm for learning hybrid Bayesian networks from data. The result of the algorithm is a network where the conditional distribution for each variable is a mixture of truncated exponentials (MTE), so that no restrictions on the network topology are imposed. The structure of the network is obtained by searching over the space of candidate networks using optimisation methods. The conditional densities are estimated by means of Gaussian kernel densities that afterwards are approximated by MTEs, so that the resulting network is appropriate for using standard algorithms for probabilistic reasoning. The behaviour of the proposed algorithm is tested using a set of real-world and artificially generated databases.
Summary. In this paper we propose a methodology for the imputation of qualitative missing data using Bayesian networks. The idea is to learn a Bayesian network from the available complete data and use it to simultaneously impute all the missing cells in a register by means of abductive inference. The proposed methodology is experimentally tested and compared with the use of classification trees.
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