Approximation of Discrete Phase-Type Distributions
ANSS '05 Proceedings of the 38th annual Symposium on Simulation
A new approach to the evaluation of non markovian stochastic petri nets
ICATPN'06 Proceedings of the 27th international conference on Applications and Theory of Petri Nets and Other Models of Concurrency
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This paper introduces a unified approach to phase-type approximation in which the discrete and the continuous phase-type models form a common model set. The models of this common set are assigned with a non-negative real parameter, the scale factor. The case when the scale factor is strictly positive results in Discrete phase-type distributions and the scale factor represents the time elapsed in one step. If the scale factor is 0, the resulting class is the class of Continuous phase-type distributions. Applying the above view, it is shown that there is no qualitative difference between the discrete and the continuous phase-type models.Based on this unified view of phase-type models one can choose the best phase-type approximation of a stochastic model by optimizing the scale factor.