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Error Control for Probabilistic Model Checking

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Book cover Verification, Model Checking, and Abstract Interpretation (VMCAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3855))

Abstract

We introduce a framework for expressing correctness guarantees of model-checking algorithms. The framework allows us to qualitatively compare different solution techniques for probabilistic model checking, both techniques based on statistical sampling and numerical computation of probability estimates. We provide several new insights into the relative merits of the different approaches. In addition, we present a new statistical solution method that can bound the probability of error under any circumstances by sometimes reporting undecided results. Previous statistical solution methods could only bound the probability of error outside of an “indifference region.”

Supported in part by the US Army Research Office (ARO), under contract no. DAAD190110485.

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Author information

Authors and Affiliations

  1. Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Håkan L. S. Younes

Authors
  1. Håkan L. S. Younes
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Editor information

Editors and Affiliations

  1. Aiken Computation Laboratory, Harvard University,, 02138, Cambridge, Mass, USA

    E. Allen Emerson

  2. Bell Laboratories, Alcatel-Lucent,  

    Kedar S. Namjoshi

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Younes, H.L.S. (2005). Error Control for Probabilistic Model Checking. In: Emerson, E.A., Namjoshi, K.S. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2006. Lecture Notes in Computer Science, vol 3855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11609773_10

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  • DOI: https://doi.org/10.1007/11609773_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31139-3

  • Online ISBN: 978-3-540-31622-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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