Abstract
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.
Translated title of the contribution | Constraint Markov Chains |
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Original language | Undefined/Unknown |
Journal | Theoretical Computer Science |
Volume | 412 |
Issue number | 34 |
Pages (from-to) | 4373-4404 |
Number of pages | 32 |
ISSN | 0304-3975 |
DOIs | |
Publication status | Published - 2011 |
Keywords
- Specification theory
- Markov Chains
- Compositional reasoning
- Abstraction
- Process algebra