  Discrete max-linear Bayesian networks are directed graphical models specified
by the same recursive structural equations as max-linear models but with
discrete innovations. When all of the random variables in the model are binary,
these models are isomorphic to the conjunctive Bayesian network (CBN) models of
Beerenwinkel, Eriksson, and Sturmfels. Many of the techniques used to study CBN
models can be extended to discrete max-linear models and similar results can be
obtained. In particular, we extend the fact that CBN models are toric varieties
after linear change of coordinates to all discrete max-linear models.
