A testing scenario for probabilistic processes
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. Categories and Subject Descriptors: F.1.1 [Computation by Abstract Devices]: Models of Computation-Automata; F.1.2 [Computation by Abstract Devices]: Modes of Computation- Probabilistic computation; F.4.3 [Mathematical Logic and Formal Languages]: Formal languages-Classes defined by grammars or automata; G.3 [Probability and Statistics]-Probabilistic algorithms, stochastic processes
testing observational equivalence probabilistic automata trace distributions approximation induction principle metric spaces theory verification concurrency theory buttonpushing scenario cpo