Learning Minimal Latent Directed Information Trees
The authors propose a framework for learning the structure of a minimal latent tree with an associated discrepancy measure. Specifically, they apply this algorithm to recover the minimal latent directed information tree on a mixture of set of observed and unobserved random processes. Directed information trees are a new type of probabilistic graphical model based on directed information that represents the casual dynamics among random processes in stochastic systems. To the best of their knowledge, this is the first approach that recovers these types of latent graphical models where samples are available only from a subset of processes.