Verification of probabilistic programs
SIAM Journal on Computing
Verification of multiprocess probabilistic protocols
Distributed Computing
Parallel program design: a foundation
Parallel program design: a foundation
Building on the unity experience: compositionality, fairness and probability in parallelism
Building on the unity experience: compositionality, fairness and probability in parallelism
Proving time bounds for randomized distributed algorithms
PODC '94 Proceedings of the thirteenth annual ACM symposium on Principles of distributed computing
Probabilistic predicate transformers
ACM Transactions on Programming Languages and Systems (TOPLAS)
Probabilistic models for the guarded command language
Science of Computer Programming - Special issue: on formal specifications: foundations, methods, tools and applications: selected papers from the FMTA '95 conference (29–31 May 1995, Konstancin n. Warsaw, Poland)
A Discipline of Programming
POPL '81 Proceedings of the 8th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Finite State Markovian Decision Processes
Finite State Markovian Decision Processes
Analyzing Expected Time by Scheduler-Luck Games
IEEE Transactions on Software Engineering
Temporal Logics for the Specification of Performance and Reliability
STACS '97 Proceedings of the 14th Annual Symposium on Theoretical Aspects of Computer Science
CONCUR '95 Proceedings of the 6th International Conference on Concurrency Theory
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Abstraction and refinement in probabilistic systems
ACM SIGMETRICS Performance Evaluation Review
Cost-based analysis of probabilistic programs mechanised in HOL
Nordic Journal of Computing
Quantitative temporal logic mechanized in HOL
ICTAC'05 Proceedings of the Second international conference on Theoretical Aspects of Computing
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In this paper we show how quantitative program logic (Morgan et al., ACM Trans. Programming Languages Systems 18 (1996) 325) provides a formal framework in which to promote standard techniques of program analysis to a context where probability and nondeterminism interact, a situation common to probabilistic distributed algorithms. We show that overall expected time can be formulated directly in the logic and that it can be derived from local properties of components. We illustrate the methods with an analysis of expected running time of the probabilistic dining philosophers (Lehmann and Ravin, Proc 8th Annu. ACM. Symp. on principles of Programming Languages, ACM, New York, 1981, p. 133).