Advances in Petri nets 1986, part II on Petri nets: applications and relationships to other models of concurrency
An improvement in formal verification
Proceedings of the 7th IFIP WG6.1 International Conference on Formal Description Techniques VII
A Compositional Trace-Based Semantics for Probabilistic Automata
CONCUR '95 Proceedings of the 6th International Conference on Concurrency Theory
Distributed Monitoring of Concurrent and Asynchronous Systems*
Discrete Event Dynamic Systems
Communication nets; stochastic message flow and delay
Communication nets; stochastic message flow and delay
Information and Computation
Projective topology on bifinite domains and applications
Theoretical Computer Science - Spatial representation: Discrete vs. continous computational models
A projective formalism applied to topological and probabilistic event structures
Mathematical Structures in Computer Science
The (true) concurrent markov property and some applications to markov nets
ICATPN'05 Proceedings of the 26th international conference on Applications and Theory of Petri Nets
FM'06 Proceedings of the 14th international conference on Formal Methods
Concurrency, σ -Algebras, and Probabilistic Fairness
FOSSACS '09 Proceedings of the 12th International Conference on Foundations of Software Science and Computational Structures: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009
Critical Paths in the Partial Order Unfolding of a Stochastic Petri Net
FORMATS '09 Proceedings of the 7th International Conference on Formal Modeling and Analysis of Timed Systems
| Hi-index | 5.24 |
We introduce the model of Markov nets, a probabilistic extension of safe Petri nets under the true-concurrency semantics-this means that traces, not firing sequences, are given a probability. This model builds upon our previous work on probabilistic event structures. We use the notion of a branching cell for event structures, and show that the latter provides an adequate conception of local state for nets. We prove a Law of Large Numbers (LLN) for Markov nets, which constitutes the main contribution of the paper. This LLN allows for the characterization, in a quantitative way, of the asymptotic behavior of Markov nets.