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.
Most Downloaded: REACTION brochure [ 20244 ]
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