# Model Checking Finite-Horizon Markov Chains with Probabilistic Inference

arXiv: https://arxiv.org/abs/2105.12326 · HTML: https://mlsanity.com/paper/2105.12326/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2021.

## Built on by (most cited first, 2 of 2)

- [flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs](https://mlsanity.com/paper/2110.10284/) (2021)
- [Tensor Probabilistic Model Checking of Finite-Horizon Markov Chains (Extended Version)](https://mlsanity.com/paper/2608.00374/) (2026)

## Abstract (arXiv)

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.