Abstract probabilistic automata
VMCAI'11 Proceedings of the 12th international conference on Verification, model checking, and abstract interpretation
Quantitative multi-objective verification for probabilistic systems
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
Decision problems for interval Markov chains
LATA'11 Proceedings of the 5th international conference on Language and automata theory and applications
p-Automata: New foundations for discrete-time probabilistic verification
Performance Evaluation
New results for Constraint Markov Chains
Performance Evaluation
Probabilistic contracts for component-based design
Formal Methods in System Design
ICTAC'12 Proceedings of the 9th international conference on Theoretical Aspects of Computing
Abstract Probabilistic Automata
Information and Computation
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Notions of specification, implementation, satisfaction, and refinement, together with operators supporting stepwise design, constitute a {specification theory}. We construct such a theory for Markov Chains (MCs) employing a new abstraction of a Constraint MC. Constraint MCs permit rich constraints on probability distributions and thus generalize prior abstractions such as Interval MCs. Linear (polynomial) constraints suffice for closure under conjunction (respectively parallel composition). This is the first specification theory for MCs with such closure properties. We discuss its relation to simpler operators for known languages such as probabilistic process algebra. Despite the generality, all operators and relations are computable.