jmlr jmlr2005 jmlr2005-44 jmlr2005-44-reference knowledge-graph by maker-knowledge-mining
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Author: Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman
Abstract: Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and statistical problems in domains that involve a large number of variables. In this paper,1 we consider a solution that is applicable when many variables have similar behavior. We introduce a new class of models, module networks, that explicitly partition the variables into modules, so that the variables in each module share the same parents in the network and the same conditional probability distribution. We define the semantics of module networks, and describe an algorithm that learns the modules’ composition and their dependency structure from data. Evaluation on real data in the domains of gene expression and the stock market shows that module networks generalize better than Bayesian networks, and that the learned module network structure reveals regularities that are obscured in learned Bayesian networks. 1. A preliminary version of this paper appeared in the Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 2003 (UAI ’03). c 2005 Eran Segal, Dana Pe’er, Aviv Regev, Daphne Koller and Nir Friedman. S EGAL , P E ’ ER , R EGEV, KOLLER AND F RIEDMAN
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