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Automatic Generation of Moment-Based Invariants for Prob-Solvable Loops

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Automated Technology for Verification and Analysis (ATVA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11781))

Abstract

One of the main challenges in the analysis of probabilistic programs is to compute invariant properties that summarise loop behaviours. Automation of invariant generation is still at its infancy and most of the times targets only expected values of the program variables, which is insufficient to recover the full probabilistic program behaviour. We present a method to automatically generate moment-based invariants of a subclass of probabilistic programs, called Prob-solvable loops, with polynomial assignments over random variables and parametrised distributions. We combine methods from symbolic summation and statistics to derive invariants as valid properties over higher-order moments, such as expected values or variances, of program variables. We successfully evaluated our work on several examples where full automation for computing higher-order moments and invariants over program variables was not yet possible.

This research was supported by the Austrian Science Fund (FWF) under grants S11405-N23, S11409-N23 (RiSE/SHiNE), the ERC Starting Grant 2014 SYMCAR 639270, the Wallenberg Academy Fellowship 2014 TheProSE and the Austrian FWF project W1255-N23.

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Notes

  1. 1.

    due to the series expansion \(e^{tX} = 1 + tE[X] + \frac{t^2E[X^2]}{2!} + \frac{t^3E[X^3]}{3!} + \dots \) and derivative w.r.t. t.

  2. 2.

    a known distribution is a distribution with known and computable moments.

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Acknowledgements

We would like to thank Joost-Pieter Katoen for his constructive feedback on a preliminary version of the manuscript.

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Correspondence to Laura Kovács .

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Bartocci, E., Kovács, L., Stankovič, M. (2019). Automatic Generation of Moment-Based Invariants for Prob-Solvable Loops. In: Chen, YF., Cheng, CH., Esparza, J. (eds) Automated Technology for Verification and Analysis. ATVA 2019. Lecture Notes in Computer Science(), vol 11781. Springer, Cham. https://doi.org/10.1007/978-3-030-31784-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-31784-3_15

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