Error control for probabilistic model checking

TL;DR: A new statistical solution method is presented that can bound the probability of error under any circumstances by sometimes reporting undecided results, and is presented as a framework for expressing correctness guarantees of model-checking algorithms.

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.”