• DOI:10.1007/978-3-030-81688-9_27
• Corpus ID: 235195879

Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

@inproceedings{Holtzen2021ModelCF,
  title={Model Checking Finite-Horizon Markov Chains with Probabilistic Inference},
  author={Steven Holtzen and Sebastian Junges and Marcell Vazquez-Chanlatte and Todd D. Millstein and Sanjit A. Seshia and Guy Van den Broeck},
  booktitle={CAV},
  year={2021}
}

• Steven Holtzen, Sebastian Junges, +3 authors Guy Van den Broeck
• Published in CAV 26 May 2021
• Computer Science

We revisit the symbolic verification of Markov chains with respect to finite horizon reachability properties. The prevalent approach iteratively computes step-bounded state reachability probabilities. By contrast, recent advances in probabilistic inference suggest symbolically representing all horizon-length paths through the Markov chain. We ask whether this perspective advances the state-of-the-art in probabilistic model checking. First, we formally describe both approaches in order to highlight their key differences. Then, using these insights we develop Rubicon, a tool that transpiles Prism models to the probabilistic inference tool Dice. Finally, we demonstrate better scalability compared to probabilistic model checkers on selected benchmarks. All together, our results suggest that probabilistic inference is a valuable addition to the probabilistic model checking portfolio, with Rubicon as a first step towards integrating both perspectives.