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Moment-Based Invariants for Probabilistic Loops with Non-polynomial Assignments

  • 2022
  • OriginalPaper
  • Chapter
Published in:
Authors
Andrey Kofnov
Marcel Moosbrugger
Miroslav Stankovič
Ezio Bartocci
Efstathia Bura
Published in
Quantitative Evaluation of Systems
Publisher
Springer International Publishing

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Abstract

The chapter delves into the application of moment-based invariants for probabilistic loops with non-polynomial assignments, a crucial aspect in AI applications, security protocols, and stochastic dynamical systems. It introduces a method that leverages polynomial chaos expansion to approximate complex dynamics, enabling the computation of moments for a broader class of stochastic systems. The approach is validated through benchmarks, demonstrating high accuracy and efficiency compared to state-of-the-art tools. The chapter also provides a comprehensive framework for understanding and implementing these techniques, making it a valuable resource for specialists in the field.
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This summary of the content was generated with the help of AI.

Supported by the Vienna Science and Technology Fund (WWTF ICT19-018), the TU Wien Doctoral College (SecInt), the FWF research projects LogiCS W1255-N23 and P 30690-N35, and the ERC Consolidator Grant ARTIST 101002685.

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next chapter Distribution Estimation for Probabilistic Loops
Title
Moment-Based Invariants for Probabilistic Loops with Non-polynomial Assignments
Authors
Andrey Kofnov
Marcel Moosbrugger
Miroslav Stankovič
Ezio Bartocci
Efstathia Bura
Copyright Year
2022
Book
Quantitative Evaluation of Systems

Print ISBN: 978-3-031-16335-7

Electronic ISBN: 978-3-031-16336-4

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-16336-4_1
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