The EAPLS Best Paper Award 2024

The EAPLS Best Paper Award 2024 is awarded to the paper

"Accurately Computing Expected Visiting Times and Stationary Distributions in Markov Chains", by Hannah Mertens, Joost-Pieter Katoen, Tim Quatmann and Tobias Winkler (RWTH Aachen University, Aachen, Germany).

This paper has been published in the proceedings of the 30th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2024). Congratulations to the authors!

The abstract of the paper:

We study the accurate and efficient computation of the expected number of times each state is visited in discrete- and continuous-time Markov chains. To obtain sound accuracy guarantees efficiently, we lift interval iteration and topological approaches known from the computation of reachability probabilities and expected rewards. We further study applications of expected visiting times, including the sound computation of the stationary distribution and expected rewards conditioned on reaching multiple goal states. The implementation of our methods in the probabilistic model checker Storm scales to large systems with millions of states. Our experiments on the quantitative verification benchmark set show that the computation of stationary distributions via expected visiting times consistently outperforms existing approaches — sometimes by several orders of magnitude.

Link to publication:

https://link.springer.com/chapter/10.1007/978-3-031-57249-4_12