We demonstrate a novel simulation technique for analysing large stochastic process algebra models, applying this to a secure electronic voting system example. By approximating the discrete state space of a PEPA model by a continuous equivalent, we can draw on rate equation simulation techniques from both chemical and biological modelling to avoid having to directly enumerate the huge state spaces involved. We use stochastic simulation techniques to provide traces of course-of-values time series representing the number of components in a particular state. Using such a technique we can get simulation results for models exceeding 10^10000 states within only a few seconds.
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