In this paper, 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.
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