Efficient Methods to Compute Optimal Tree Approximations of Directed Information Graphs
Recently, directed information graphs have been proposed as concise graphical representations of the statistical dynamics amongst multiple random processes. A directed edge from one node to another indicates that the past of one random process statistically affects the future of another, given the past of all other processes. When the number of processes is large, computing those conditional dependence tests becomes difficult. Also, when the number of interactions becomes too large, the graph no longer facilitates visual extraction of relevant information for decision-making.