Error control for probabilistic model checking

Abstract

We introduce a framework for expressing correctness guarantees of model-checking algorithms. The framework allows us to qualitatively compare different solution techniques for probabilistic model checking, both techniques based on statistical sampling and numerical computation of probability estimates. We provide several new insights into the relative merits of the different approaches. In addition, we present a new statistical solution method that can bound the probability of error under any circumstances by sometimes reporting undecided results. Previous statistical solution methods could only bound the probability of error outside of an “indifference region.”

Sample citation

Håkan L. S. Younes. 2006. Error control for probabilistic model checking. In Proceedings of the 7th International Conference on Verification, Model Checking, and Abstract Interpretation, edited by E. Allen Emerson and Kedar S. Namjoshi, vol. 3855 of Lecture Notes in Computer Science, 142–156, Charleston, South Carolina.Springer