Scoring and Searching over Bayesian Networks with Causal and Associative Priors
This work addresses the challenge of integrating expert knowledge into Bayesian network learning, which is incremental but useful for domains with prior experimental data.
The paper tackled the problem of learning Bayesian networks by incorporating prior beliefs about causal and associative paths, resulting in improved skeleton and edge direction learning as shown in experiments.
A significant theoretical advantage of search-and-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.