Canonical models are useful not only because they simplify the
construction of probabilistic models, but also because they save storage
space and computational time, and because they respond to causal
patterns that can exploited to generate user explanations.
In this paper we offer a general framework for canonical models and
briefly analyze the properties of the OR/MAX family of models.
The general framework can be easily used to generate other canonical models.
