  A large amount of observational data has been accumulated in various fields
in recent times, and there is a growing need to estimate the generating
processes of these data. A linear non-Gaussian acyclic model (LiNGAM) based on
the non-Gaussianity of external influences has been proposed to estimate the
data-generating processes of variables. However, the results of the estimation
can be biased if there are latent classes. In this paper, we first review
LiNGAM, its extended model, as well as the estimation procedure for LiNGAM in a
Bayesian framework. We then propose a new Bayesian estimation procedure that
solves the problem.
