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On Probabilistic Program Equivalence and Refinement

  • Andrzej S. Murawski
  • Joël Ouaknine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3653)

Abstract

We study notions of equivalence and refinement for probabilistic programs formalized in the second-order fragment of Probabilistic Idealized Algol. Probabilistic programs implement randomized algorithms: a given input yields a probability distribution on the set of possible outputs. Intuitively, two programs are equivalent if they give rise to identical distributions for all inputs. We show that equivalence is decidable by studying the fully abstract game semantics of probabilistic programs and relating it to probabilistic finite automata. For terms in β-normal form our decision procedure runs in time exponential in the syntactic size of programs; it is moreover fully compositional in that it can handle open programs (probabilistic modules with unspecified components).

In contrast, we show that the natural notion of program refinement, in which the input-output distributions of one program uniformly dominate those of the other program, is undecidable.

Keywords

Probabilistic Program Probabilistic Idealize Interaction Sequence Outgoing Transition Natural Notion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Andrzej S. Murawski
    • 1
  • Joël Ouaknine
    • 1
  1. 1.Oxford University Computing LaboratoryOxfordUK

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