Explaining the reasoning of Bayesian networks
Jordy van Leersum, Silja Renooij, Sjoerd T. Timmer · 2015
This thesis presents a method to create an explanation for the reasoning of Bayesian networks in order to explain the most probable value for the node of interest. The first part identifies a set of intermediate nodes which are nodes that can explain the most probable value of the node of interest. These intermediate nodes act as a funnel for the node of interest and summarize the evidence nodes. This set of intermediate nodes is found by the Edmonds-Karp algorithm in combination with one of three weight-assignment functions. One of these functions is purely based on the structure of the graph of the Bayesian network and the other two different functions take the probability distributions of the Bayesian network into account as well. The second part gives arguments that explain the most probable values of the intermediate nodes by creating clusters, which are set of nodes that include at least one evidence node of the Bayesian network. The actual explanation takes the output of these two methods to construct an explanation which is an interactive web page, where the explanation of the most probable values of the nodes are supported by verbal expressions and graphical figures.