Probabilistic Abstract Interpretation

  • Patrick Cousot
  • Michael Monerau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7211)

Abstract

Abstract interpretation has been widely used for verifying properties of computer systems. Here, we present a way to extend this framework to the case of probabilistic systems.

The probabilistic abstraction framework that we propose allows us to systematically lift any classical analysis or verification method to the probabilistic setting by separating in the program semantics the probabilistic behavior from the (non-)deterministic behavior. This separation provides new insights for designing novel probabilistic static analyses and verification methods.

We define the concrete probabilistic semantics and propose different ways to abstract them. We provide examples illustrating the expressiveness and effectiveness of our approach.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Patrick Cousot
    • 1
  • Michael Monerau
    • 1
  1. 1.NYU and École Normale SupérieureCourant InstituteFrance

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