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Aiming low is harder: induction for lower bounds in probabilistic program verification

Authors:

Benjamin Lucien Kaminski,

Joost-Pieter KatoenAuthors Info & Claims

Proceedings of the ACM on Programming Languages, Volume 4, Issue POPL

Article No.: 37, Pages 1 - 28

https://doi.org/10.1145/3371105

Published: 20 December 2019 Publication History

Abstract

We present a new inductive rule for verifying lower bounds on expected values of random variables after execution of probabilistic loops as well as on their expected runtimes. Our rule is simple in the sense that loop body semantics need to be applied only finitely often in order to verify that the candidates are indeed lower bounds. In particular, it is not necessary to find the limit of a sequence as in many previous rules.

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Cited By

Haselwarter PLi Kde Medeiros MGregersen SAguirre ATassarotti JBirkedal L(2024)Tachis: Higher-Order Separation Logic with Credits for Expected CostsProceedings of the ACM on Programming Languages10.1145/36897538:OOPSLA2(1189-1218)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689753
Zhang LZilberstein NKaminski BSilva A(2024)Quantitative Weakest Hyper Pre: Unifying Correctness and Incorrectness Hyperproperties via Predicate TransformersProceedings of the ACM on Programming Languages10.1145/36897408:OOPSLA2(817-845)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689740
Kura SUnno H(2024)Automated Verification of Higher-Order Probabilistic Programs via a Dependent Refinement Type SystemProceedings of the ACM on Programming Languages10.1145/36746628:ICFP(973-1002)Online publication date: 15-Aug-2024
https://dl.acm.org/doi/10.1145/3674662
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Index Terms

Aiming low is harder: induction for lower bounds in probabilistic program verification
1. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic algorithms
    2. Stochastic processes
      1. Markov processes
2. Theory of computation
  1. Semantics and reasoning
    1. Program semantics
      1. Denotational semantics

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cover image Proceedings of the ACM on Programming Languages

Proceedings of the ACM on Programming Languages Volume 4, Issue POPL

January 2020

1984 pages

EISSN:2475-1421

DOI:10.1145/3377388

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Copyright © 2019 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Publication History

Published: 20 December 2019

Published in PACMPL Volume 4, Issue POPL

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Cited By

Haselwarter PLi Kde Medeiros MGregersen SAguirre ATassarotti JBirkedal L(2024)Tachis: Higher-Order Separation Logic with Credits for Expected CostsProceedings of the ACM on Programming Languages10.1145/36897538:OOPSLA2(1189-1218)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689753
Zhang LZilberstein NKaminski BSilva A(2024)Quantitative Weakest Hyper Pre: Unifying Correctness and Incorrectness Hyperproperties via Predicate TransformersProceedings of the ACM on Programming Languages10.1145/36897408:OOPSLA2(817-845)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689740
Kura SUnno H(2024)Automated Verification of Higher-Order Probabilistic Programs via a Dependent Refinement Type SystemProceedings of the ACM on Programming Languages10.1145/36746628:ICFP(973-1002)Online publication date: 15-Aug-2024
https://dl.acm.org/doi/10.1145/3674662
Chatterjee KGoharshady ENovotný PŽikelić Đ(2024)Equivalence and Similarity Refutation for Probabilistic ProgramsProceedings of the ACM on Programming Languages10.1145/36564628:PLDI(2098-2122)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656462
Klinkenberg LBlumenthal CChen MHaase DKatoen J(2024)Exact Bayesian Inference for Loopy Probabilistic Programs using Generating FunctionsProceedings of the ACM on Programming Languages10.1145/36498448:OOPSLA1(923-953)Online publication date: 29-Apr-2024
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Batz KBiskup TKatoen JWinkler T(2024)Programmatic Strategy Synthesis: Resolving Nondeterminism in Probabilistic ProgramsProceedings of the ACM on Programming Languages10.1145/36329358:POPL(2792-2820)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3632935
Moosbrugger MMüllner JBartocci EKovács L(2024)Polar: An Algebraic Analyzer for (Probabilistic) LoopsPrinciples of Verification: Cycling the Probabilistic Landscape10.1007/978-3-031-75783-9_8(179-200)Online publication date: 13-Nov-2024
https://doi.org/10.1007/978-3-031-75783-9_8
Batz KKaminski BMatheja CWinkler T(2024)J-P: MDP. FP. PPPrinciples of Verification: Cycling the Probabilistic Landscape10.1007/978-3-031-75783-9_11(255-302)Online publication date: 13-Nov-2024
https://doi.org/10.1007/978-3-031-75783-9_11
Feng SYang TChen MZhan N(2024)A Unified Framework for Quantitative Analysis of Probabilistic ProgramsPrinciples of Verification: Cycling the Probabilistic Landscape10.1007/978-3-031-75783-9_10(230-254)Online publication date: 13-Nov-2024
https://doi.org/10.1007/978-3-031-75783-9_10
Schröer PBatz KKaminski BKatoen JMatheja C(2023)A Deductive Verification Infrastructure for Probabilistic ProgramsProceedings of the ACM on Programming Languages10.1145/36228707:OOPSLA2(2052-2082)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622870
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