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## Sensitivity Analysis of Markov Regenerative Stochastic Petri Nets.

Mainkar, Varsha;
Choi, Hoon;
Trivedi, Kishor S.
In:
*5th International Workshop on Petri Nets and Performance Models, Toulouse (F) 19.-22. October 1993*, pages 180-189.
1993.

Abstract:
Sensitivity analysis, i.e., the analysis of the effect of small variations
in system parameters on the output measures can be studied by computing
the derivatives of the output measures with respect to the parameter. This
paper presents an algorithm for parametric sensitivity analysis of Markov
Regenerative Stochastic Petri Nets (MRSPN). MRSPNs are a true
generalization of stochastic Petri nets, in that they allow for
transitions to have generally distributed firing times (under certain
conditions). We extend the steady state analysis and present equations for
sensitivity of the steady state probabilities with respect to an arbitrary
system parameter. Sensitivity functions of the performance measures can
accordingly be expressed in terms of the sensitivity functions of the
steady state probabilities. We present an application of our algorithm by
finding an optimizing parameter for a vacation queue

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