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For pt.I see ibid., p.720-5 (1997). We present an original construction of stochastic Petri nets (PN) dedicated to large distributed discrete event systems. Its main characteristic is to provide statistically independent behaviors to concurrent (parallel) processes of the system. We end up with "hybrid" model where only some events are randomized, and that can't be described by a standard Markov dynamics. Equivalently, time is only partially ordered in such systems. Then assuming that every fired transition produces a random label we address the problem of finding the most likely path in the net, given a sequence of such labels. This problem is usually solved by dynamic programming on the state-space (marking graph of the PN). The proposed approach instead is based the net unfolding.