Accelerating Parametric Probabilistic Verification
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Abstract (arXiv)
We present a novel method for computing reachability probabilities of parametric discrete-time Markov chains whose transition probabilities are fractions of polynomials over a set of parameters. Our algorithm is based on two key ingredients: a graph decomposition into strongly connected subgraphs combined with a novel factorization strategy for polynomials. Experimental evaluations show that these approaches can lead to a speed-up of up to several orders of magnitude in comparison to existing approaches
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Dec 2013.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 3.
6 papers.
- 2022Abstraction-Refinement for Hierarchical Probabilistic Modelscited by 6
- 2021Fine-Tuning the Odds in Bayesian Networkscited by 3
- 2022Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checkingcited by 0
- 2026PAC Approximation and DIRECT Optimization for Parametric Markov Modelscited by 0
- 2026Leveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verificationcited by 0
- 2026Supermartingale Certificates for Parametric MDPscited by 0
- 2026Supermartingale Certificates for Parametric MDPs11 Sep 2026
- 2026Leveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verification4 Aug 2026
- 2026PAC Approximation and DIRECT Optimization for Parametric Markov Models3 Aug 2026
- 2022Abstraction-Refinement for Hierarchical Probabilistic Models6 Jun 2022
- 2022Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking11 May 2022
- 2021Fine-Tuning the Odds in Bayesian Networks29 May 2021