Experiments for 'Efficient Sensitivity Analysis for Parametric Robust Markov Chains'

This artifact accompanies the CAV 2023 paper with the title 'Efficient Sensitivity Analysis for Parametric Robust Markov Chains'. The artifact contains a docker file, which can be unzipped and then loaded with: docker load -i prmc_sensitivity_cav23_docker.tar Depending on your permissions, you may need to run this command with sudo. Please refer to the ReadMe for more information. The source code of the docker container is available on GitHub: https://github.com/LAVA-LAB/prmc-sensitivity.

Country

Netherlands

Related Organizations

Radboud University Nijmegen
Netherlands
Technical University Eindhoven
Netherlands
Eindhoven University of Technology
Netherlands
The University of Texas at Austin
United States

Keywords

Probabilistic verification, Robust Markov chains, Parametric Markov chains, Sensitivity analysis, Convex optimization

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average