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- W1892040981 abstract "Perturbation analysis in probabilistic verification addresses the robustness and sensitivity problem for verification of stochastic models against qualitative and quantitative properties. We identify two types of perturbation bounds, namely non-asymptotic bounds and asymptotic bounds. Non-asymptotic bounds are exact, pointwise bounds that quantify the upper and lower bounds of the verification result subject to a given perturbation of the model, whereas asymptotic bounds are closed-form bounds that approximate non-asymptotic bounds by assuming that the given perturbation is sufficiently small. We perform perturbation analysis in the setting of Discrete-time Markov Chains. We consider three basic matrix norms to capture the perturbation distance, and focus on the computational aspect. Our main contributions include algorithms and tight complexity bounds for calculating both non-asymptotic bounds and asymptotic bounds with respect to the three perturbation distances." @default.
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- W1892040981 date "2014-01-01" @default.
- W1892040981 modified "2023-09-28" @default.
- W1892040981 title "Perturbation Analysis in Verification of Discrete-Time Markov Chains" @default.
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- W1892040981 doi "https://doi.org/10.1007/978-3-662-44584-6_16" @default.
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