A two-phase method for extracting explanatory arguments from Bayesian networks

We formalise a two-phase method for extracting probabilistically supported arguments from a Bayesian network. First, from a Bayesian network we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the relation between hypotheses and evidence that is modelled in the Bayesian network.