  We define the concept of dependence among multiple variables using maximum
entropy techniques and introduce a graphical notation to denote the
dependencies. Direct inference of information theoretic quantities from data
uncovers dependencies even in undersampled regimes when the joint probability
distribution cannot be reliably estimated. The method is tested on synthetic
data. We anticipate it to be useful for inference of genetic circuits and other
biological signaling networks.
