  We build information geometry for a partially ordered set of variables and
define the orthogonal decomposition of information theoretic quantities. The
natural connection between information geometry and order theory leads to
efficient decomposition algorithms. This generalization of Amari's seminal work
on hierarchical decomposition of probability distributions on event
combinations enables us to analyze high-order statistical interactions arising
in neuroscience, biology, and machine learning.
