DiPS: A Tool for Data-Informed Parameter Synthesis for Markov Chains from Multiple-Property Specifications
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Proceedings |
| Conference | Performance Engineering and Stochastic Modeling |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-030-91825-5_5 |
| Keywords | probabilistic model checking; parameter synthesis; DTMC |
| Description | 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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