Accelerated Model Checking of Parametric Markov Chains

  • Paul Gainer
  • Ernst Moritz HahnEmail author
  • Sven Schewe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11138)


Parametric Markov chains occur quite naturally in various applications: they can be used for a conservative analysis of probabilistic systems (no matter how the parameter is chosen, the system works to specification); they can be used to find optimal settings for a parameter; they can be used to visualise the influence of system parameters; and they can be used to make it easy to adjust the analysis for the case that parameters change. Unfortunately, these advancements come at a cost: parametric model checking is—or rather was—often slow. To make the analysis of parametric Markov models scale, we need three ingredients: clever algorithms, the right data structure, and good engineering. Clever algorithms are often the main (or sole) selling point; and we face the trouble that this paper focuses on – the latter ingredients to efficient model checking. Consequently, our easiest claim to fame is in the speed-up we have often realised when comparing to the state of the art.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of LiverpoolLiverpoolUK

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