Name
Abstract | ||
|---|---|---|
We introduce a notion of finite testing, based on statistical hypothesis tests, via a variant of the well-known trace machine. Under this scenario, two processes are deemed observationally equivalent if they cannot be distinguished by any finite test. We consider processes modeled as image finite probabilistic automata and prove that our notion of observational equivalence coincides with the trace distribution equivalence proposed by Segala. Along the way, we give an explicit characterization of the set of probabilistic generalize the Approximation Induction Principle by defining an also prove limit and convex closure properties of trace distributions in an appropriate metric space. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1314690.1314693 | Journal of the ACM |
Keywords | DocType | Volume |
additional key words and phrases: concurrency theory,cpo,trace distributions,well-known trace machine,approximation induction principle,metric spaces,observational equivalence,testing scenario,convex closure property,trace distribution,button pushing scenario,probabilistic automata,finite test,image finite probabilistic automaton,appropriate metric space,approximation induction princi- ple,probabilistic process,testing,finite testing,trace distribution equivalence | Journal | 54 |
Issue | ISSN | Citations |
6 | 0004-5411 | 31 |
PageRank | References | Authors |
0.97 | 26 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Cheung | 1 | 485 | 21.39 |
| Mariëlle Stoelinga | 2 | 716 | 53.71 |
| Frits Vaandrager | 3 | 1571 | 105.12 |