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)
Termination of Probabilistic Concurrent Program
ACM Transactions on Programming Languages and Systems (TOPLAS)
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Quantitative program logic and expected time bounds in probabilistic distributed algorithms
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LICS '03 Proceedings of the 18th Annual IEEE Symposium on Logic in Computer Science
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Abstraction, Refinement And Proof For Probabilistic Systems (Monographs in Computer Science)
Abstraction, Refinement And Proof For Probabilistic Systems (Monographs in Computer Science)
Cost-based analysis of probabilistic programs mechanised in HOL
Nordic Journal of Computing
Proof rules for probabilistic loops
FAC-RW'96 Proceedings of the BCS-FACS 7th conference on Refinement
Merlin: specification inference for explicit information flow problems
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
Security, Probability and Nearly Fair Coins in the Cryptographers' Café
FM '09 Proceedings of the 2nd World Congress on Formal Methods
Constructive development of probabilistic programs
FSEN'11 Proceedings of the 4th IPM international conference on Fundamentals of Software Engineering
A UTP semantics of pGCL as a homogeneous relation
IFM'12 Proceedings of the 9th international conference on Integrated Formal Methods
Algorithmic probabilistic game semantics
Formal Methods in System Design
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We summarise a verification method for probabilistic systems that is based on abstraction and refinement, and extends traditional assertional styles of verification.The approach makes extensive use of the expectation transformers of pGCL [17, 16, 13], a compact probabilistic programming language with an associated logic of real-valued functions. Analysis of large systems is made tractable by abstraction which, together with algebraic and logical reasoning, results in strong and general guarantees about probabilistic-system properties.Although our examples are specific (to pGCL), our overall goal in this note is to advocate the hierarchical development of probabilistic programs via levels of abstraction, connected by refinement, and to illustrate the proof obligations incurred by such an approach.