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Titlebook: Quantitative Evaluation of Systems; 16th International C David Parker,Verena Wolf Conference proceedings 2019 Springer Nature Switzerland A

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21#
發(fā)表于 2025-3-25 05:49:26 | 只看該作者
0302-9743 UK, in September 2019..The 17 full papers presented together with 2 short papers were carefully reviewed and selected from 40 submissions. The papers cover topics in the field of Probabilistic Verification; Learning and Verification; Hybrid Systems; Security; Probabilistic Modelling and Abstraction;
22#
發(fā)表于 2025-3-25 07:57:48 | 只看該作者
23#
發(fā)表于 2025-3-25 14:37:54 | 只看該作者
End-User Probabilistic Programmingow how uncertain values can propagate uncertainty through spreadsheets, and how sheet-defined functions can be applied to handle uncertainty. Hence, we draw conclusions about the promise and limitations of probabilistic programming for end-users.
24#
發(fā)表于 2025-3-25 17:42:16 | 只看該作者
25#
發(fā)表于 2025-3-25 22:19:36 | 只看該作者
Neural Network Precision Tuningmore than a percentage chosen by the user. From a technical point of view, we generate a system of linear constraints among integer variables that we can solve by linear programming. The solution to this system is the new precision of the neurons. We present experimental results obtained by using our method.
26#
發(fā)表于 2025-3-26 03:14:31 | 只看該作者
27#
發(fā)表于 2025-3-26 06:48:27 | 只看該作者
28#
發(fā)表于 2025-3-26 10:22:51 | 只看該作者
29#
發(fā)表于 2025-3-26 13:50:27 | 只看該作者
Model Checking Constrained Markov Reward Models with Uncertaintiest or parameter valuations satisfying a given property in the form of a quantifier-free first-order formula in the linear theory of the reals..Our work finds applications in model repair, where parameters need to be tuned so as to satisfy the desired specification, as well as in robustness analysis in the presence of stochastic perturbations.
30#
發(fā)表于 2025-3-26 18:26:52 | 只看該作者
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