Transient analysis of large Markov models with absorbing states using regenerative randomization
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Cita com:
hdl:2117/21508
Tipus de documentArticle
Data publicació2005-10
Condicions d'accésAccés obert
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Abstract
In this article, we develop a new method, called regenerative randomization, for
the transient analysis of continuous time Markov models with absorbing states.
The method has the same good properties as standard randomization: numerical
stability, well-controlled computation error, and ability to specify the computation
error in advance. The method has a benign behavior for large t and is significantly
less costly than standard randomization for large enough models and large enough t.
For a class of models, class C, including typical failure/repair reliability models
with exponential failure and repair time distributions and repair in every state with
failed components, stronger theoretical results are available assessing the efficiency
of the method in terms of “visible” model characteristics. A large example belonging
to that class is used to illustrate the performance of the method and to show that it
can indeed be much faster than standard randomization.
CitacióCarrasco, J. Transient analysis of large Markov models with absorbing states using regenerative randomization. "Communications in statistics. Simulation and computation", Octubre 2005, vol. 34, núm. 4, p. 1027-1052.
ISSN0361-0918
Versió de l'editorhttp://dx.doi.org/10.1080/03610910500308586
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