The gamma model and its discipline of programming
Science of Computer Programming
Selected papers of the Second Workshop on Concurrency and compositionality
Embedding as a tool for language comparison
Information and Computation
KLAIM: A Kernel Language for Agents Interaction and Mobility
IEEE Transactions on Software Engineering
Science of Computer Programming
Stochastic processes
Handbook of Process Algebra
Embeddings Among Concurrent Programming Languages (Preliminary Version)
CONCUR '92 Proceedings of the Third International Conference on Concurrency Theory
On the Operational Sematics of a Coordination Language
ECOOP '94 Selected papers from the ECOOP'94 Workshop on Models and Languages for Coordination of Parallelism and Distribution, Object-Based Models and Languages for Concurrent Systems
Gamma and the Chemical Reaction Model: Fifteen Years After
WMP '00 Proceedings of the Workshop on Multiset Processing: Multiset Processing, Mathematical, Computer Science, and Molecular Computing Points of View
Comparing the expressive power of the synchronous and asynchronous $pi$-calculi
Mathematical Structures in Computer Science
Abstract interpretation for worst and average case analysis
Program analysis and compilation, theory and practice
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The Chemical Abstract Machine (CHAM) of Berry and Boudol provides a commonly accepted, uniform framework for describing the operational semantics of various process calculi and languages, such as for example CCS, the π calculus and coordination languages like Linda. In its original form the CHAM is purely non-deterministic and thus only describes what reactions are possible but not how long it will take (in the average) before a certain reaction takes place or its probability. Such quantitative information is however often vital for “real world” applications such as systems biology or performance analysis. We propose a probabilistic version of the CHAM. We then define a linear operator semantics for the probabilistic CHAM which exploits a tensor product representation for distributions over possible solutions. Based on this we propose a novel approach towards comparing the expressive power of different calculi via their encoding in the probabilistic CHAM. We illustrate our approach by comparing the expressiveness of various Linda Languages.