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The REACTION project is a 4-year project started in 2010. It is partly funded by the European Commission under the 7th Framework Programme in the area of Personal Health Systems under Grant Agreement no. 248590

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Downloads: Scoring and Searching over Bayesian Networks with Informative, Causal and Associative Priors

Downloads Home > Scientific conference papers > Scoring and Searching over Bayesian Networks with Informative, Causal and Associative Priors

Scoring and Searching over Bayesian Networks with Informative, Causal and Associative Priors


A significant theoretical advantage of searchand-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus
complementing the data. In this paper, a method is presented for assigning priors based on beliefs on the presence or absence of certain paths in the true network. Such beliefs correspond to knowledge about the possible causal and associative relations between pairs of variables. This type of knowledge naturally arises from prior experimental and
observational data, among others. In addition, a novel search-operator is proposed to take advantage of such prior knowledge. Experiments show that, using path beliefs improves the learning of the skeleton, as well as the edge directions in the network.
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09. August 2013 10:53
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