  Recent work by the authors equips Petri occurrence nets (PN) with probability
distributions which fully replace nondeterminism. To avoid the so-called
confusion problem, the construction imposes additional causal dependencies
which restrict choices within certain subnets called structural branching cells
(s-cells). Bayesian nets (BN) are usually structured as partial orders where
nodes define conditional probability distributions. In the paper, we unify the
two structures in terms of Symmetric Monoidal Categories (SMC), so that we can
apply to PN ordinary analysis techniques developed for BN. Interestingly, it
turns out that PN which cannot be SMC-decomposed are exactly s-cells. This
result confirms the importance for Petri nets of both SMC and s-cells.
