A testing scenario for probabilistic processes

Ling Chueng, Mariëlle Stoelinga, Frits Vaandrager

    Research output: Contribution to journalArticleAcademicpeer-review

    36 Citations (Scopus)


    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.
    Original languageEnglish
    Article number29
    Number of pages45
    JournalJournal of the Association for Computing Machinery
    Issue numberSuppl. 6
    Publication statusPublished - Dec 2007


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