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Synthesizing Probabilistic Invariants via Doob’s Decomposition

  • Gilles Barthe
  • Thomas Espitau
  • Luis María Ferrer Fioriti
  • Justin Hsu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9779)

Abstract

When analyzing probabilistic computations, a powerful approach is to first find a martingale—an expression on the program variables whose expectation remains invariant—and then apply the optional stopping theorem in order to infer properties at termination time. One of the main challenges, then, is to systematically find martingales.

We propose a novel procedure to synthesize martingale expressions from an arbitrary initial expression. Contrary to state-of-the-art approaches, we do not rely on constraint solving. Instead, we use a symbolic construction based on Doob’s decomposition. This procedure can produce very complex martingales, expressed in terms of conditional expectations.

We show how to automatically generate and simplify these martingales, as well as how to apply the optional stopping theorem to infer properties at termination time. This last step typically involves some simplification steps, and is usually done manually in current approaches. We implement our techniques in a prototype tool and demonstrate our process on several classical examples. Some of them go beyond the capability of current semi-automatic approaches.

Keywords

Conditional Expectation Program Variable Probabilistic Program Proof Rule Symbolic Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank the anonymous reviewers for their helpful comments. This work was partially supported by NSF grants TWC-1513694 and CNS-1065060, and by a grant from the Simons Foundation (\(\#360368\) to Justin Hsu).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gilles Barthe
    • 1
  • Thomas Espitau
    • 2
  • Luis María Ferrer Fioriti
    • 3
  • Justin Hsu
    • 4
  1. 1.IMDEA Software InstituteMadridSpain
  2. 2.ENS CachanCachanFrance
  3. 3.Saarland UniversitySaarbrückenGermany
  4. 4.University of PennsylvaniaPhiladelphiaUSA

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