Typed model counting expands model counting of propositional formulas by the ability to distinguish between certain types of models. Formally, we incorporate elements of a commutative monoid that represent these model types directly into the propositional formulas. An advantage of this approach is the ability of preserving information about which parts of a formula are satisfied by a certain type of model. We exploit this benefit when applying typed model counting to probabilistic conditional reasoning at maximum entropy. In particular, we address the task of determining the conditional structure induced by a reasoner’s probabilistic conditional knowledge base in order to draw nonmonotonic inferences based on the maximum entropy distribution.