Free choice Petri nets
Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
An Efficient Method for Generating Discrete Random Variables with General Distributions
ACM Transactions on Mathematical Software (TOMS)
Quantitative Methods in Parallel Systems
Quantitative Methods in Parallel Systems
Blocking a transition in a free choice net and what it tells about its throughput
Journal of Computer and System Sciences
Backward Coupling in Bounded Free-Choice Nets Under Markovian and Non-Markovian Assumptions
Discrete Event Dynamic Systems
Perfect simulation and non-monotone Markovian systems
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
Stationary solution approximation using a memory-efficient perfect sampling technique
Proceedings of the 44th Annual Simulation Symposium
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In this paper, we show how to design a perfect sampling algorithm for stochastic Free-Choice Petri nets by backward coupling. For Markovian event graphs, the simulation time can be greatly reduced by using extremal initial states, namely blocking marking, although such nets do not exhibit any natural monotonicity property. Another approach for perfect simulation of non-Markovian event graphs is based on a (max,plus) representation of the system and the theory of (max,plus) stochastic systems. Next, we show how to extend this approach to one-bounded free choice nets to the expense of keeping all states. Finally, experimental runs show that the (max,plus) approach needs a larger simulation time than the Markovian approach.