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Implicit data structures for logic and stochastic systems analysis

Both logic and stochastic analysis have strong theoretical underpinnings, but they have been traditionally relegated to separate areas of computer science, the former focusing on logic and discrete algorithms, the latter on exact or approximate numerical methods. In the last few years, though, there has been a convergence of research in these two areas, due to the realization that data structures used in one area can benefit the other and that, by merging the goals of the two areas, a more integrated approach to system analysis can be derived. In this paper, we describe some of the beneficial interactions between the two, and some of the research challenges ahead.