GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables
  Finding the structure of a graphical model has been received much attention
in many fields. Recently, it is reported that the non-Gaussianity of data
enables us to identify the structure of a directed acyclic graph without any
prior knowledge on the structure. In this paper, we propose a novel
non-Gaussianity based algorithm for more general type of models; chain graphs.
The algorithm finds an ordering of the disjoint subsets of variables by
iteratively evaluating the independence between the variable subset and the
residuals when the remaining variables are regressed on those. However, its
computational cost grows exponentially according to the number of variables.
Therefore, we further discuss an efficient approximate approach for applying
the algorithm to large sized graphs. We illustrate the algorithm with
artificial and real-world datasets.
