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@inproceedings{1845004,
author = {Hajnal, Matej and Petrov, Tatjana and Šafránek, David},
address = {Cham},
booktitle = {Performance Engineering and Stochastic Modeling},
doi = {http://dx.doi.org/10.1007/978-3-030-91825-5_5},
editor = {Ballarini et al.},
keywords = {probabilistic model checking; parameter synthesis; DTMC},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Cham},
isbn = {978-3-030-91824-8},
pages = {79-95},
publisher = {Springer},
title = {DiPS: A Tool for Data-Informed Parameter Synthesis for Markov Chains from Multiple-Property Specifications},
year = {2021}
}
TY - JOUR
ID - 1845004
AU - Hajnal, Matej - Petrov, Tatjana - Šafránek, David
PY - 2021
TI - DiPS: A Tool for Data-Informed Parameter Synthesis for Markov Chains from Multiple-Property Specifications
PB - Springer
CY - Cham
SN - 9783030918248
KW - probabilistic model checking
KW - parameter synthesis
KW - DTMC
N2 - 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.
ER -
HAJNAL, Matej, Tatjana PETROV and David ŠAFRÁNEK. DiPS: A Tool for Data-Informed Parameter Synthesis for Markov Chains from Multiple-Property Specifications. In Ballarini et al. \textit{Performance Engineering and Stochastic Modeling}. Cham: Springer, 2021, p.~79-95. ISBN~978-3-030-91824-8. Available from: https://dx.doi.org/10.1007/978-3-030-91825-5\_{}5.
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