1994
DOI: 10.1016/0166-5316(94)90021-3
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Markov regenerative stochastic Petri nets

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Cited by 216 publications
(115 citation statements)
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“…Non-Markovian applications may be represented using a higher order state space [55], where the level of history retained in the model needs to be determined empirically. Markov Regenerative Stochastic Petri Nets (MRSPNs) [10] may be used to represent generally distributed and deterministic transitions, whereas Fluid Stochastic Petri Nets (FSPNs) [50] may be used to keep track of the time spent in each component during testing to evaluate the impact of testing and repair of each component on application reliability. Colored Petri Nets (CPNs) [44] may be used to represent the dependencies among components that arise due to error propagation.…”
Section: Modeling Limitationsmentioning
confidence: 99%
“…Non-Markovian applications may be represented using a higher order state space [55], where the level of history retained in the model needs to be determined empirically. Markov Regenerative Stochastic Petri Nets (MRSPNs) [10] may be used to represent generally distributed and deterministic transitions, whereas Fluid Stochastic Petri Nets (FSPNs) [50] may be used to keep track of the time spent in each component during testing to evaluate the impact of testing and repair of each component on application reliability. Colored Petri Nets (CPNs) [44] may be used to represent the dependencies among components that arise due to error propagation.…”
Section: Modeling Limitationsmentioning
confidence: 99%
“…As originally observed by Choi et al [6], the underlying marking process is a Markov regenerative process under the conditions that:…”
Section: Markov Regenerative Stochastic Petri Netsmentioning
confidence: 92%
“…Due to transitions with GEN distribution, the process can accumulate memory over time, producing different classes of stochastic processes depending on the presence of regeneration points, i.e. states where all GEN timers lose their memory [2,4]. This basically depends on the conditions of persistence of GEN transitions.…”
Section: Underlying Stochastic Processmentioning
confidence: 99%
“…Analysis methods for this class of models have been finely developed relying on Markov renewal theory [4,5] and on the method of supplementary variables [9,6,10].…”
Section: Underlying Stochastic Processmentioning
confidence: 99%
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