Publication details

DiPS: A Tool for Data-Informed Parameter Synthesis for Markov Chains from Multiple-Property Specifications

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Authors

HAJNAL Matej PETROV Tatjana ŠAFRÁNEK David

Year of publication 2021
Type Article in Proceedings
Conference Performance Engineering and Stochastic Modeling
MU Faculty or unit

Faculty of Informatics

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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