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Abstraction of Expectation Functions Using Gaussian Distributions

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Verification, Model Checking, and Abstract Interpretation (VMCAI 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2575))

Abstract

We consider semantics of infinite-state programs, both probabilistic and nondeterministic, as expectation functions: for any set of states A, we associate to each program point a function mapping each state to its expectation of starting a trace reaching A. We then compute a safe upper approximation of these functions using abstract interpretation. This computation takes place in an abstract domain of extended Gaussian (normal) distributions. Category: 1 (new results)

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Author information

Authors and Affiliations

  1. LIENS, 45 rue d’Ulm, 75230, Paris cedex 5, France

    David Monniaux

Authors
  1. David Monniaux
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Editor information

Editors and Affiliations

  1. Department of Computer Science, New York University, 715 Broadway (7th floor), NY 10003, NewYork, USA

    Lenore D. Zuck

  2. College of Computer Science, Northeastern University, 360 Huntington Ave., 02115, Boston, MA, USA

    Paul C. Attie

  3. Computer Science Department, Venice University C’Foscari, Via Torino 155, 30170, Mestre-Venezia, Italy

    Agostino Cortesi

  4. Department of Computer Science and Electrical Engineering, West Virginia University, 26505, Morgantown, WV, USA

    Supratik Mukhopadhyay

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© 2003 Springer-Verlag Berlin Heidelberg

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Monniaux, D. (2003). Abstraction of Expectation Functions Using Gaussian Distributions. In: Zuck, L.D., Attie, P.C., Cortesi, A., Mukhopadhyay, S. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2003. Lecture Notes in Computer Science, vol 2575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36384-X_15

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  • DOI: https://doi.org/10.1007/3-540-36384-X_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00348-9

  • Online ISBN: 978-3-540-36384-2

  • eBook Packages: Springer Book Archive

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