Abstract
Several schemes have been provided in Statistical Model Checking (SMC) for the estimation of property occurrence based on predefined confidence and absolute or relative error. Simulations might be however costly if many samples are required and the usual algorithms implemented in statistical model checkers tend to be conservative. Bayesian and rare event techniques can be used to reduce the sample size but they can not be applied without prerequisite or knowledge about the system under scrutiny. Recently, sequential algorithms based on Monte Carlo estimations and Massart bounds have been proposed to reduce the sample size while providing guarantees on error bounds which has been shown to outperform alternative frequentist approaches [15]. In this work, we discuss some features regarding the distribution and the optimisation of these algorithms.
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Notes
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- 2.
The Okamoto bound is sometimes called the Chernoff bound in the literature.
- 3.
A journal version with the proofs is currently submitted [14]. The proofs are also available here: https://www.researchgate.net/publication/317823195_Sequenti-al_Schemes_for_Frequentist_Estimation_of_Properties_in_Statistical_Model_Checking.
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Acknowledgment
This work was supported in part by the National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its National Cybersecurity R&D Programme (Award No. NRF2014NCR-NCR001-040) and administered by the National Cybersecurity R&D Directorate.
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Jegourel, C., Sun, J., Dong, J.S. (2018). On the Sequential Massart Algorithm for Statistical Model Checking. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Verification. ISoLA 2018. Lecture Notes in Computer Science(), vol 11245. Springer, Cham. https://doi.org/10.1007/978-3-030-03421-4_19
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