期刊
PERFORMANCE ENGINEERING AND STOCHASTIC MODELING
卷 13104, 期 -, 页码 79-95出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-91825-5_5
关键词
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资金
- Ministry of Science, Research and the Arts of the state of Baden-Wurttemberg
- DFG Centre of Excellence [2117, 422037984]
- AFF (Committee on Research, University of Konstanz)
- Young Scholar Fund (YSF) [P83943018FP430/18]
- Max Planck Institute of Animal Behaviour
- Czech Grant Agency [GA18-00178S]
The tool presented combines existing methods for parameter synthesis of Discrete-time Markov chains, with several hybrid methods for parameter space exploration. Users can choose between different parameter exploration methods based on trade-offs between scalability and inference quality. Its performance has been evaluated on several benchmarks.
We present a tool for inferring the parameters of a Discrete-time Markov chain (DTMC) with respect to properties written in probabilistic temporal logic (PCTL) informed by data observations. The tool combines, in a modular and user-friendly way, the existing methods and tools for parameter synthesis of DTMCs. On top of this, the tool implements several hybrid methods for the exploration of the parameter space based on utilising the intermediate results of parametric model checking - the symbolic representation of properties' satisfaction in the form of rational functions. These methods are combined to support three different parameter exploration methods: (i) optimisation, (ii) parameter synthesis, (iii) Bayesian parameter inference. Each of the available methods makes a different trade-off between scalability and inference quality, which can be chosen by the user depending on the application context. In this paper, we present the implementation, the main features of the tool, and we evaluate its performance on several benchmarks.
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