Discovering Structure in Continuous Variables Using Bayesian Networks

Part of Advances in Neural Information Processing Systems 8 (NIPS 1995)

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Reimar Hofmann, Volker Tresp


We study Bayesian networks for continuous variables using non(cid:173) linear conditional density estimators. We demonstrate that use(cid:173) ful structures can be extracted from a data set in a self-organized way and we present sampling techniques for belief update based on Markov blanket conditional density models.