In the setting of a simple process language featuring non-deterministic choice and a parallel operator on the one hand and probabilistic choice on the other hand, we propose an axiomatization capturing strong distribution bisimulation. Contrary to other process equivalences for probabilistic process languages, in this paper distributions rather than states are the leading ingredients for building the semantics and the accompanying equational theory, for which we establish soundness and completeness.
|Title of host publication||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Editors||Maurice H. ter Beek, Alessandro Fantechi, Laura Semini|
|Place of Publication||Cham|
|Number of pages||15|
|Publication status||Published - 2019|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|