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- W119678319 abstract "A discrete-event stochastic system makes state transitions when events associated with the occupied state occur; events occur only at an increasing sequence of random times. The underlying stochastic process of a discrete-event system has to record the state of the system as it evolves over continuous time. The generalized semi-Markov process provides a framework for specifying and analyzing stochastic processes underlying most kinds of discrete-event systems; see e.g., Glynn [G1]. A generalized semi-Markov process is a continuous-time stochastic process that makes a state transition when one or more “events” associated with the occupied state occur. Events associated with a state compete to trigger the next state transition, and each set of trigger events has its own distribution for determining the next state. At each state transition of the generalized semi-Markov process, new events may be scheduled. For each of these new events, a clock indicating the time until the event is scheduled to occur is set according to an independent (stochastic) mechanism. If a scheduled event is not in the set of events that triggers a state transition but is associated with the next state, its clock continues to run; if such an event is not associated with the next state, it is cancelled, and the corresponding clock reading is discarded. The generalized semi-Markov process is a continuous-time stochastic process {S(t): t ≥ 0} that records the state of the system as it evolves over time." @default.
- W119678319 created "2016-06-24" @default.
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- W119678319 date "1997-01-01" @default.
- W119678319 modified "2023-09-25" @default.
- W119678319 title "Parallel Numerical Solution of a Class of Volterra Integro—Differential Equations" @default.
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- W119678319 doi "https://doi.org/10.1007/978-1-4612-1980-4_10" @default.
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